Aula Módulo 6.ipynb

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":1,"outputs":[{"output_type":"stream","text":"/kaggle/input/covid19/Kaggle_Sirio_Libanes_ICU_Prediction.xlsx\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install openpyxl","execution_count":2,"outputs":[{"output_type":"stream","text":"Requirement already satisfied: openpyxl in /opt/conda/lib/python3.7/site-packages (3.0.6)\r\nRequirement already satisfied: et-xmlfile in /opt/conda/lib/python3.7/site-packages (from openpyxl) (1.0.1)\r\nRequirement already satisfied: jdcal in /opt/conda/lib/python3.7/site-packages (from openpyxl) (1.4.1)\r\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# Módulo 6 - Machine Learning: Modelos, Métricas e Validação"},{"metadata":{},"cell_type":"markdown","source":"## Aula 01: Workflow de Machine Learning "},{"metadata":{},"cell_type":"markdown","source":"**Wokflow**:\n\n* **Objetivo e Impacto no Negócio**: \n    * Prever a necessidade do uso da UTI para os pacientes do Hospital Sírio-Libanês.\n    * Preparar os hospitais para previnir lotação da UTI.\n     \n    \n\n* **Aquisição e Transformação dos dados**: \n    * Em nosso caso, a aquisição e o tratamento dos dados já foram realizados pela equipe do Sirio-Libanês.\n    * Deve ser feita a aquisição dos dados em uma Data Warehouse ou por *web-scrapping*.\n    * Escolha e engenharia de features mais importantes para a modelagem.\n    * Escalonamento e normalização (caso necessário) para o uso do dataset no modelo.\n    \n    \n    \n* **Desenvolvimento do Modelo**:\n    * Nas aulas de agora;\n    \n\n\n* **Comunicação dos Resultados:**\n    * O modelo é ético? Traz resultados válidos/impactos pra empresa?\n    \n\n\n* **Deploy e Monitoramento**\n    * Verificar o funcionamento do modelo"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Import nos dados\n\ndf = pd.read_excel('/kaggle/input/covid19/Kaggle_Sirio_Libanes_ICU_Prediction.xlsx')\n\ndf.head()","execution_count":3,"outputs":[{"output_type":"execute_result","execution_count":3,"data":{"text/plain":"   PATIENT_VISIT_IDENTIFIER  AGE_ABOVE65 AGE_PERCENTIL  GENDER  \\\n0                         0            1          60th       0   \n1                         0            1          60th       0   \n2                         0            1          60th       0   \n3                         0            1          60th       0   \n4                         0            1          60th       0   \n\n   DISEASE GROUPING 1  DISEASE GROUPING 2  DISEASE GROUPING 3  \\\n0                 0.0                 0.0                 0.0   \n1                 0.0                 0.0                 0.0   \n2                 0.0                 0.0                 0.0   \n3                 0.0                 0.0                 0.0   \n4                 0.0                 0.0                 0.0   \n\n   DISEASE GROUPING 4  DISEASE GROUPING 5  DISEASE GROUPING 6  ...  \\\n0                 0.0                 1.0                 1.0  ...   \n1                 0.0                 1.0                 1.0  ...   \n2                 0.0                 1.0                 1.0  ...   \n3                 0.0                 1.0                 1.0  ...   \n4                 0.0                 1.0                 1.0  ...   \n\n   TEMPERATURE_DIFF  OXYGEN_SATURATION_DIFF  BLOODPRESSURE_DIASTOLIC_DIFF_REL  \\\n0         -1.000000               -1.000000                         -1.000000   \n1         -1.000000               -1.000000                         -1.000000   \n2               NaN                     NaN                               NaN   \n3         -1.000000               -1.000000                               NaN   \n4         -0.238095               -0.818182                         -0.389967   \n\n   BLOODPRESSURE_SISTOLIC_DIFF_REL  HEART_RATE_DIFF_REL  \\\n0                        -1.000000            -1.000000   \n1                        -1.000000            -1.000000   \n2                              NaN                  NaN   \n3                              NaN                  NaN   \n4                         0.407558            -0.230462   \n\n   RESPIRATORY_RATE_DIFF_REL  TEMPERATURE_DIFF_REL  \\\n0                  -1.000000             -1.000000   \n1                  -1.000000             -1.000000   \n2                        NaN                   NaN   \n3                        NaN             -1.000000   \n4                   0.096774             -0.242282   \n\n   OXYGEN_SATURATION_DIFF_REL    WINDOW  ICU  \n0                   -1.000000       0-2    0  \n1                   -1.000000       2-4    0  \n2                         NaN       4-6    0  \n3                   -1.000000      6-12    0  \n4                   -0.814433  ABOVE_12    1  \n\n[5 rows x 231 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PATIENT_VISIT_IDENTIFIER</th>\n      <th>AGE_ABOVE65</th>\n      <th>AGE_PERCENTIL</th>\n      <th>GENDER</th>\n      <th>DISEASE GROUPING 1</th>\n      <th>DISEASE GROUPING 2</th>\n      <th>DISEASE GROUPING 3</th>\n      <th>DISEASE GROUPING 4</th>\n      <th>DISEASE GROUPING 5</th>\n      <th>DISEASE GROUPING 6</th>\n      <th>...</th>\n      <th>TEMPERATURE_DIFF</th>\n      <th>OXYGEN_SATURATION_DIFF</th>\n      <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n      <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n      <th>HEART_RATE_DIFF_REL</th>\n      <th>RESPIRATORY_RATE_DIFF_REL</th>\n      <th>TEMPERATURE_DIFF_REL</th>\n      <th>OXYGEN_SATURATION_DIFF_REL</th>\n      <th>WINDOW</th>\n      <th>ICU</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>60th</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>1</td>\n      <td>60th</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>2-4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1</td>\n      <td>60th</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4-6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>1</td>\n      <td>60th</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>6-12</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>1</td>\n      <td>60th</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>-0.238095</td>\n      <td>-0.818182</td>\n      <td>-0.389967</td>\n      <td>0.407558</td>\n      <td>-0.230462</td>\n      <td>0.096774</td>\n      <td>-0.242282</td>\n      <td>-0.814433</td>\n      <td>ABOVE_12</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 231 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Função helper para retornar ao ponto do último módulo e focar nos conteúdos desse\n\ndef preenche_tabela(df):\n    features_continuas_colunas = df.iloc[:, 13:-2].columns\n    features_continuas = df.groupby(\"PATIENT_VISIT_IDENTIFIER\", as_index=False)[features_continuas_colunas].fillna(method='bfill').fillna(method='ffill')\n    \n    features_categoricas = df.iloc[:, :13]\n    saida = df.iloc[:, -2:]\n    dados_finais = pd.concat([features_categoricas, features_continuas, saida], ignore_index=True, axis=1)\n    dados_finais.columns = df.columns\n    \n    return dados_finais","execution_count":4,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Dropando as linhas em que pacientes entravam direto pra UTI\ndf_limpo = preenche_tabela(df)\na_remover = df_limpo.query(\"WINDOW=='0-2' and ICU==1\")['PATIENT_VISIT_IDENTIFIER'].values\ndf_limpo.query(\"PATIENT_VISIT_IDENTIFIER not in @a_remover\")\ndf_limpo = df_limpo.dropna()\n\ndf_limpo.describe()","execution_count":5,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"       PATIENT_VISIT_IDENTIFIER  AGE_ABOVE65       GENDER  DISEASE GROUPING 1  \\\ncount               1920.000000  1920.000000  1920.000000         1920.000000   \nmean                 191.981771     0.468750     0.369792            0.108333   \nstd                  111.312587     0.499152     0.482874            0.310882   \nmin                    0.000000     0.000000     0.000000            0.000000   \n25%                   95.750000     0.000000     0.000000            0.000000   \n50%                  191.500000     0.000000     0.000000            0.000000   \n75%                  288.250000     1.000000     1.000000            0.000000   \nmax                  384.000000     1.000000     1.000000            1.000000   \n\n       DISEASE GROUPING 2  DISEASE GROUPING 3  DISEASE GROUPING 4  \\\ncount         1920.000000         1920.000000         1920.000000   \nmean             0.028125            0.097917            0.019792   \nstd              0.165373            0.297279            0.139320   \nmin              0.000000            0.000000            0.000000   \n25%              0.000000            0.000000            0.000000   \n50%              0.000000            0.000000            0.000000   \n75%              0.000000            0.000000            0.000000   \nmax              1.000000            1.000000            1.000000   \n\n       DISEASE GROUPING 5  DISEASE GROUPING 6          HTN  ...  \\\ncount         1920.000000         1920.000000  1920.000000  ...   \nmean             0.128125            0.046875     0.213021  ...   \nstd              0.334316            0.211426     0.409549  ...   \nmin              0.000000            0.000000     0.000000  ...   \n25%              0.000000            0.000000     0.000000  ...   \n50%              0.000000            0.000000     0.000000  ...   \n75%              0.000000            0.000000     0.000000  ...   \nmax              1.000000            1.000000     1.000000  ...   \n\n       RESPIRATORY_RATE_DIFF  TEMPERATURE_DIFF  OXYGEN_SATURATION_DIFF  \\\ncount            1920.000000       1920.000000             1920.000000   \nmean               -0.758150         -0.812388               -0.910214   \nstd                 0.433588          0.296775                0.255728   \nmin                -1.000000         -1.000000               -1.000000   \n25%                -1.000000         -1.000000               -1.000000   \n50%                -1.000000         -1.000000               -1.000000   \n75%                -0.705882         -0.690476               -0.919192   \nmax                 1.000000          1.000000                1.000000   \n\n       BLOODPRESSURE_DIASTOLIC_DIFF_REL  BLOODPRESSURE_SISTOLIC_DIFF_REL  \\\ncount                       1920.000000                      1920.000000   \nmean                          -0.818008                        -0.759122   \nstd                            0.301726                         0.389598   \nmin                           -1.000000                        -1.000000   \n25%                           -1.000000                        -1.000000   \n50%                           -1.000000                        -1.000000   \n75%                           -0.710805                        -0.622483   \nmax                            1.000000                         1.000000   \n\n       HEART_RATE_DIFF_REL  RESPIRATORY_RATE_DIFF_REL  TEMPERATURE_DIFF_REL  \\\ncount          1920.000000                1920.000000           1920.000000   \nmean             -0.848720                  -0.771187             -0.813295   \nstd               0.247117                   0.401430              0.295619   \nmin              -1.000000                  -1.000000             -1.000000   \n25%              -1.000000                  -1.000000             -1.000000   \n50%              -1.000000                  -1.000000             -1.000000   \n75%              -0.764487                  -0.725806             -0.698390   \nmax               1.000000                   1.000000              1.000000   \n\n       OXYGEN_SATURATION_DIFF_REL          ICU  \ncount                 1920.000000  1920.000000  \nmean                    -0.910085     0.268229  \nstd                      0.256368     0.443153  \nmin                     -1.000000     0.000000  \n25%                     -1.000000     0.000000  \n50%                     -1.000000     0.000000  \n75%                     -0.918403     1.000000  \nmax                      1.000000     1.000000  \n\n[8 rows x 229 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PATIENT_VISIT_IDENTIFIER</th>\n      <th>AGE_ABOVE65</th>\n      <th>GENDER</th>\n      <th>DISEASE GROUPING 1</th>\n      <th>DISEASE GROUPING 2</th>\n      <th>DISEASE GROUPING 3</th>\n      <th>DISEASE GROUPING 4</th>\n      <th>DISEASE GROUPING 5</th>\n      <th>DISEASE GROUPING 6</th>\n      <th>HTN</th>\n      <th>...</th>\n      <th>RESPIRATORY_RATE_DIFF</th>\n      <th>TEMPERATURE_DIFF</th>\n      <th>OXYGEN_SATURATION_DIFF</th>\n      <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n      <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n      <th>HEART_RATE_DIFF_REL</th>\n      <th>RESPIRATORY_RATE_DIFF_REL</th>\n      <th>TEMPERATURE_DIFF_REL</th>\n      <th>OXYGEN_SATURATION_DIFF_REL</th>\n      <th>ICU</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>...</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n      <td>1920.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>191.981771</td>\n      <td>0.468750</td>\n      <td>0.369792</td>\n      <td>0.108333</td>\n      <td>0.028125</td>\n      <td>0.097917</td>\n      <td>0.019792</td>\n      <td>0.128125</td>\n      <td>0.046875</td>\n      <td>0.213021</td>\n      <td>...</td>\n      <td>-0.758150</td>\n      <td>-0.812388</td>\n      <td>-0.910214</td>\n      <td>-0.818008</td>\n      <td>-0.759122</td>\n      <td>-0.848720</td>\n      <td>-0.771187</td>\n      <td>-0.813295</td>\n      <td>-0.910085</td>\n      <td>0.268229</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>111.312587</td>\n      <td>0.499152</td>\n      <td>0.482874</td>\n      <td>0.310882</td>\n      <td>0.165373</td>\n      <td>0.297279</td>\n      <td>0.139320</td>\n      <td>0.334316</td>\n      <td>0.211426</td>\n      <td>0.409549</td>\n      <td>...</td>\n      <td>0.433588</td>\n      <td>0.296775</td>\n      <td>0.255728</td>\n      <td>0.301726</td>\n      <td>0.389598</td>\n      <td>0.247117</td>\n      <td>0.401430</td>\n      <td>0.295619</td>\n      <td>0.256368</td>\n      <td>0.443153</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>95.750000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>191.500000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>288.250000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>-0.705882</td>\n      <td>-0.690476</td>\n      <td>-0.919192</td>\n      <td>-0.710805</td>\n      <td>-0.622483</td>\n      <td>-0.764487</td>\n      <td>-0.725806</td>\n      <td>-0.698390</td>\n      <td>-0.918403</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>384.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 229 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Deixando apenas a janela 0-2 e, caso o paciente for internado em algum momento depois, ICU == 1\n\ndef prepare_window(rows):\n    if(np.any(rows['ICU'])):\n        rows.loc[rows[\"WINDOW\"]==\"0-2\", \"ICU\"] = 1\n    return rows.loc[rows[\"WINDOW\"] == \"0-2\"]\n\ndf_limpo = df_limpo.groupby(\"PATIENT_VISIT_IDENTIFIER\").apply(prepare_window)","execution_count":6,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Import das libs do sklearn \n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.dummy import DummyClassifier\nfrom sklearn.metrics import accuracy_score","execution_count":7,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Gerando um DummyClassifier() como baseline\nnp.random.seed(42)\n\n# Pegar apenas colunas continuas\nx_columns = df.describe().columns\n\ny = df_limpo['ICU']\nX = df_limpo[x_columns].drop([\"ICU\"], axis=1)\n\nX_test, X_train, y_test, y_train = train_test_split(X, y, stratify = y)\n\ndummy_model = DummyClassifier().fit(X_train, y_train)\ny_pred = dummy_model.predict(X_test)\n\naccuracy_score(y_test, y_pred)","execution_count":8,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n  \"stratified to prior in 0.24.\", FutureWarning)\n","name":"stderr"},{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"0.5034722222222222"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Gerando um LogisticRegression como baseline (melhor)\n\nlr_model = LogisticRegression(max_iter = 2000).fit(X_train, y_train)\ny_pred = lr_model.predict(X_test)\n\naccuracy_score(y_test, y_pred)","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"0.7256944444444444"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Havíamos utilizado o describe para selecionar colunas contínuas. Vamos ver que colunas ficaram de fora:"},{"metadata":{"trusted":true},"cell_type":"code","source":"for coluna in df.columns:\n    if coluna not in x_columns:\n        print(coluna)","execution_count":10,"outputs":[{"output_type":"stream","text":"AGE_PERCENTIL\nWINDOW\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"A coluna `WINDOW` não é relevante para o modelo (só estamos utilizando dados de uma mesma janela). Já a `AGE_PERCENTIL` é bastante.\nVamos transforma-la em uma coluna útil ao modelo:"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_desafio = df_limpo.copy()\n\ndf_limpo.AGE_PERCENTIL = df_limpo.AGE_PERCENTIL.astype(\"category\").cat.codes","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_limpo","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"                               PATIENT_VISIT_IDENTIFIER  AGE_ABOVE65  \\\nPATIENT_VISIT_IDENTIFIER                                               \n0                        0                            0            1   \n1                        5                            1            1   \n2                        10                           2            0   \n3                        15                           3            0   \n4                        20                           4            0   \n...                                                 ...          ...   \n380                      1900                       380            0   \n381                      1905                       381            1   \n382                      1910                       382            0   \n383                      1915                       383            0   \n384                      1920                       384            0   \n\n                               AGE_PERCENTIL  GENDER  DISEASE GROUPING 1  \\\nPATIENT_VISIT_IDENTIFIER                                                   \n0                        0                 5       0                 0.0   \n1                        5                 8       1                 0.0   \n2                        10                0       0                 0.0   \n3                        15                3       1                 0.0   \n4                        20                0       0                 0.0   \n...                                      ...     ...                 ...   \n380                      1900              3       1                 0.0   \n381                      1905              9       0                 0.0   \n382                      1910              4       0                 0.0   \n383                      1915              3       1                 0.0   \n384                      1920              4       1                 0.0   \n\n                               DISEASE GROUPING 2  DISEASE GROUPING 3  \\\nPATIENT_VISIT_IDENTIFIER                                                \n0                        0                    0.0                 0.0   \n1                        5                    0.0                 0.0   \n2                        10                   0.0                 0.0   \n3                        15                   0.0                 0.0   \n4                        20                   0.0                 0.0   \n...                                           ...                 ...   \n380                      1900                 0.0                 0.0   \n381                      1905                 0.0                 0.0   \n382                      1910                 0.0                 0.0   \n383                      1915                 0.0                 0.0   \n384                      1920                 0.0                 0.0   \n\n                               DISEASE GROUPING 4  DISEASE GROUPING 5  \\\nPATIENT_VISIT_IDENTIFIER                                                \n0                        0                    0.0                 1.0   \n1                        5                    0.0                 0.0   \n2                        10                   0.0                 0.0   \n3                        15                   0.0                 0.0   \n4                        20                   0.0                 0.0   \n...                                           ...                 ...   \n380                      1900                 0.0                 0.0   \n381                      1905                 0.0                 0.0   \n382                      1910                 0.0                 0.0   \n383                      1915                 0.0                 0.0   \n384                      1920                 0.0                 0.0   \n\n                               DISEASE GROUPING 6  ...  TEMPERATURE_DIFF  \\\nPATIENT_VISIT_IDENTIFIER                           ...                     \n0                        0                    1.0  ...         -1.000000   \n1                        5                    0.0  ...         -1.000000   \n2                        10                   0.0  ...         -1.000000   \n3                        15                   0.0  ...         -1.000000   \n4                        20                   0.0  ...         -0.952381   \n...                                           ...  ...               ...   \n380                      1900                 0.0  ...         -1.000000   \n381                      1905                 0.0  ...         -0.619048   \n382                      1910                 0.0  ...         -1.000000   \n383                      1915                 0.0  ...         -1.000000   \n384                      1920                 0.0  ...         -1.000000   \n\n                               OXYGEN_SATURATION_DIFF  \\\nPATIENT_VISIT_IDENTIFIER                                \n0                        0                  -1.000000   \n1                        5                  -1.000000   \n2                        10                 -0.959596   \n3                        15                 -1.000000   \n4                        20                 -0.979798   \n...                                               ...   \n380                      1900               -1.000000   \n381                      1905               -1.000000   \n382                      1910               -1.000000   \n383                      1915               -1.000000   \n384                      1920               -1.000000   \n\n                               BLOODPRESSURE_DIASTOLIC_DIFF_REL  \\\nPATIENT_VISIT_IDENTIFIER                                          \n0                        0                            -1.000000   \n1                        5                            -1.000000   \n2                        10                           -0.515528   \n3                        15                           -1.000000   \n4                        20                           -1.000000   \n...                                                         ...   \n380                      1900                         -1.000000   \n381                      1905                         -1.000000   \n382                      1910                         -1.000000   \n383                      1915                         -1.000000   \n384                      1920                         -1.000000   \n\n                               BLOODPRESSURE_SISTOLIC_DIFF_REL  \\\nPATIENT_VISIT_IDENTIFIER                                         \n0                        0                           -1.000000   \n1                        5                           -1.000000   \n2                        10                          -0.351328   \n3                        15                          -1.000000   \n4                        20                          -0.883669   \n...                                                        ...   \n380                      1900                        -1.000000   \n381                      1905                        -1.000000   \n382                      1910                        -1.000000   \n383                      1915                        -1.000000   \n384                      1920                        -1.000000   \n\n                               HEART_RATE_DIFF_REL  RESPIRATORY_RATE_DIFF_REL  \\\nPATIENT_VISIT_IDENTIFIER                                                        \n0                        0               -1.000000                  -1.000000   \n1                        5               -1.000000                  -1.000000   \n2                        10              -0.747001                  -0.756272   \n3                        15              -1.000000                  -1.000000   \n4                        20              -0.956805                  -0.870968   \n...                                            ...                        ...   \n380                      1900            -1.000000                  -1.000000   \n381                      1905            -1.000000                  -1.000000   \n382                      1910            -1.000000                  -1.000000   \n383                      1915            -1.000000                  -1.000000   \n384                      1920            -1.000000                  -1.000000   \n\n                               TEMPERATURE_DIFF_REL  \\\nPATIENT_VISIT_IDENTIFIER                              \n0                        0                -1.000000   \n1                        5                -1.000000   \n2                        10               -1.000000   \n3                        15               -1.000000   \n4                        20               -0.953536   \n...                                             ...   \n380                      1900             -1.000000   \n381                      1905             -0.612627   \n382                      1910             -1.000000   \n383                      1915             -1.000000   \n384                      1920             -1.000000   \n\n                               OXYGEN_SATURATION_DIFF_REL  WINDOW  ICU  \nPATIENT_VISIT_IDENTIFIER                                                \n0                        0                      -1.000000     0-2    1  \n1                        5                      -1.000000     0-2    1  \n2                        10                     -0.961262     0-2    1  \n3                        15                     -1.000000     0-2    0  \n4                        20                     -0.980333     0-2    0  \n...                                                   ...     ...  ...  \n380                      1900                   -1.000000     0-2    1  \n381                      1905                   -1.000000     0-2    0  \n382                      1910                   -1.000000     0-2    1  \n383                      1915                   -1.000000     0-2    0  \n384                      1920                   -1.000000     0-2    0  \n\n[384 rows x 231 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>PATIENT_VISIT_IDENTIFIER</th>\n      <th>AGE_ABOVE65</th>\n      <th>AGE_PERCENTIL</th>\n      <th>GENDER</th>\n      <th>DISEASE GROUPING 1</th>\n      <th>DISEASE GROUPING 2</th>\n      <th>DISEASE GROUPING 3</th>\n      <th>DISEASE GROUPING 4</th>\n      <th>DISEASE GROUPING 5</th>\n      <th>DISEASE GROUPING 6</th>\n      <th>...</th>\n      <th>TEMPERATURE_DIFF</th>\n      <th>OXYGEN_SATURATION_DIFF</th>\n      <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n      <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n      <th>HEART_RATE_DIFF_REL</th>\n      <th>RESPIRATORY_RATE_DIFF_REL</th>\n      <th>TEMPERATURE_DIFF_REL</th>\n      <th>OXYGEN_SATURATION_DIFF_REL</th>\n      <th>WINDOW</th>\n      <th>ICU</th>\n    </tr>\n    <tr>\n      <th>PATIENT_VISIT_IDENTIFIER</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <th>5</th>\n      <td>1</td>\n      <td>1</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <th>10</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-0.959596</td>\n      <td>-0.515528</td>\n      <td>-0.351328</td>\n      <td>-0.747001</td>\n      <td>-0.756272</td>\n      <td>-1.000000</td>\n      <td>-0.961262</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <th>15</th>\n      <td>3</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <th>20</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-0.952381</td>\n      <td>-0.979798</td>\n      <td>-1.000000</td>\n      <td>-0.883669</td>\n      <td>-0.956805</td>\n      <td>-0.870968</td>\n      <td>-0.953536</td>\n      <td>-0.980333</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>380</th>\n      <th>1900</th>\n      <td>380</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>381</th>\n      <th>1905</th>\n      <td>381</td>\n      <td>1</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-0.619048</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-0.612627</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>382</th>\n      <th>1910</th>\n      <td>382</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>383</th>\n      <th>1915</th>\n      <td>383</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>384</th>\n      <th>1920</th>\n      <td>384</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>384 rows × 231 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Agora podemos rodar o modelo novamente:"},{"metadata":{"trusted":true},"cell_type":"code","source":"x_columns = df.describe().columns\n\ny = df_limpo['ICU']\nX = df_limpo[x_columns].drop([\"ICU\"], axis=1)\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y)\n\ndummy_model = DummyClassifier().fit(X_train, y_train)\ny_pred = dummy_model.predict(X_test)\n\naccuracy_score(y_test, y_pred)","execution_count":13,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n  \"stratified to prior in 0.24.\", FutureWarning)\n","name":"stderr"},{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"0.4895833333333333"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"lr_model = LogisticRegression(max_iter = 2000).fit(X_train, y_train)\ny_pred = lr_model.predict(X_test)\n\naccuracy_score(y_test, y_pred)","execution_count":14,"outputs":[{"output_type":"execute_result","execution_count":14,"data":{"text/plain":"0.7291666666666666"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"#### Desafio 01: Transformar a coluna AGE_PERCENTIL em dados categóricos, mas utilizando outro método."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Irei utilizar o pd.get_dummies para resolver o desafio:\ndf_desafio = pd.get_dummies(df_desafio, columns=['AGE_PERCENTIL'], prefix=['AGE_PERCENTIL'])","execution_count":15,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_desafio.iloc[:, -11:].sample(5)","execution_count":16,"outputs":[{"output_type":"execute_result","execution_count":16,"data":{"text/plain":"                               ICU  AGE_PERCENTIL_10th  AGE_PERCENTIL_20th  \\\nPATIENT_VISIT_IDENTIFIER                                                     \n325                      1625    1                   0                   0   \n332                      1660    0                   0                   0   \n87                       435     1                   0                   0   \n88                       440     1                   1                   0   \n261                      1305    1                   0                   0   \n\n                               AGE_PERCENTIL_30th  AGE_PERCENTIL_40th  \\\nPATIENT_VISIT_IDENTIFIER                                                \n325                      1625                   0                   0   \n332                      1660                   0                   0   \n87                       435                    0                   0   \n88                       440                    0                   0   \n261                      1305                   0                   0   \n\n                               AGE_PERCENTIL_50th  AGE_PERCENTIL_60th  \\\nPATIENT_VISIT_IDENTIFIER                                                \n325                      1625                   0                   0   \n332                      1660                   0                   0   \n87                       435                    0                   0   \n88                       440                    0                   0   \n261                      1305                   1                   0   \n\n                               AGE_PERCENTIL_70th  AGE_PERCENTIL_80th  \\\nPATIENT_VISIT_IDENTIFIER                                                \n325                      1625                   1                   0   \n332                      1660                   0                   1   \n87                       435                    0                   0   \n88                       440                    0                   0   \n261                      1305                   0                   0   \n\n                               AGE_PERCENTIL_90th  AGE_PERCENTIL_Above 90th  \nPATIENT_VISIT_IDENTIFIER                                                     \n325                      1625                   0                         0  \n332                      1660                   0                         0  \n87                       435                    0                         1  \n88                       440                    0                         0  \n261                      1305                   0                         0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>ICU</th>\n      <th>AGE_PERCENTIL_10th</th>\n      <th>AGE_PERCENTIL_20th</th>\n      <th>AGE_PERCENTIL_30th</th>\n      <th>AGE_PERCENTIL_40th</th>\n      <th>AGE_PERCENTIL_50th</th>\n      <th>AGE_PERCENTIL_60th</th>\n      <th>AGE_PERCENTIL_70th</th>\n      <th>AGE_PERCENTIL_80th</th>\n      <th>AGE_PERCENTIL_90th</th>\n      <th>AGE_PERCENTIL_Above 90th</th>\n    </tr>\n    <tr>\n      <th>PATIENT_VISIT_IDENTIFIER</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>325</th>\n      <th>1625</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>332</th>\n      <th>1660</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <th>435</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <th>440</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>261</th>\n      <th>1305</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Aula 02 - Métricas de Avaliação\n\nEssa aula será dividida em 2 vídeos e um extra:\n* Video 01: Matriz de Confusão\n* Video 02: Curva ROC e AUC\n* Para saber mais: Árvore de Decisão"},{"metadata":{},"cell_type":"markdown","source":"### Video 01 - Matriz de Confusão"},{"metadata":{},"cell_type":"markdown","source":"#### Desafio 02: Verificar o impacto da mudança nos resultados."},{"metadata":{"trusted":true},"cell_type":"code","source":"y = df_desafio['ICU']\nX = df_desafio.drop([\"ICU\", \"WINDOW\"], axis=1)\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y)\n\nlr_model = LogisticRegression(max_iter = 2000).fit(X_train, y_train)\ny_pred = lr_model.predict(X_test)\n\naccuracy_score(y_test, y_pred)","execution_count":17,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"0.71875"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"#### Voltando pra aula\n\nAgora, vamos observar a diferença na acurácia de outro modelo (árvore de decisão) e aprender a decidir qual a melhor métrica de avaliação para cada tipo de modelo."},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.tree import DecisionTreeClassifier\n\ntree_model = DecisionTreeClassifier().fit(X_train, y_train)\n\ny_pred = tree_model.predict(X_test)\naccuracy_score(y_test, y_pred)","execution_count":18,"outputs":[{"output_type":"execute_result","execution_count":18,"data":{"text/plain":"0.6666666666666666"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import plot_confusion_matrix\nimport matplotlib.pyplot as plt\n\nplot_confusion_matrix(tree_model, X_test, y_test)","execution_count":19,"outputs":[{"output_type":"execute_result","execution_count":19,"data":{"text/plain":"<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f1eb711b610>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"A matriz de confusão é relativamente simples de entender. No **eixo X**, estão os valores que foram previstos pelo nosso modelo, enquanto no **eixo Y** estão os **dados verdadeiros**. \n\nSeguindo isso, podemos observar que o nosso modelo preveu 43 pacientes como `ICU == 0`, com 29 corretos (True Negatives) e 14 incorretos (False Negatives). No oposto, o modelo preveu que 53 pacientes do dataset precisariam da UTI. Desses, ele acertou 35 (True Positives) e errou 18 (False Positives). \n\nPara o nosso problema, é importante minimizar os **False Negatives**. Se um paciente que precisa da UTI é mandado para casa, pode ser que ocorra um agravamento da doença."},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import classification_report\n\nprint(classification_report(y_test, y_pred))","execution_count":20,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.67      0.62      0.64        47\n           1       0.66      0.71      0.69        49\n\n    accuracy                           0.67        96\n   macro avg       0.67      0.67      0.67        96\nweighted avg       0.67      0.67      0.67        96\n\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"* Precision\n    * De tudo que o modelo preveu como positivo (True Positives + False Positives), quantos são realmente corretos (TP / TP + FP).\n    * Essa métrica deve ser utilizada quando o Falso Positivo é a principal preocupação do modelo.\n\n\n\n* Recall\n    * De tudo que o modelo preveu (True Positives + False Negative), quantos são realmente corretos (TP / TP + FN).\n    * Essa métrica deve ser utilizada quando o Falso Negativo é a principal preocupação do modelo (como é o nosso caso).\n    \n    \n* F1 - Score\n    * Média harmônica entre a Precisão e Recall"},{"metadata":{},"cell_type":"markdown","source":"#### Desafio 03 - Pesquisar sobre o F-score\nEm análise estátistica de classificações binárias, o F-score é uma métrica de avaliação da acurácia do modelo. O mais comum, o **F1-score** é calculado utilizando a média harmônica da **precisão** e do **recall** do modelo. Além dele, podemos generalizar utilizando o **Fβ-score** (β que normalmente leva valores de 2 ou 0.5) em que β é escolhido de forma que o recall é considerado β vezes mais importante que a precisão.\n\n\n#### Desafio 04 - Plotar a árvore de decisão\n\nVia Kaggle "},{"metadata":{},"cell_type":"markdown","source":"### Video 02 - Curva ROC e AUC\n\nA curva ROC é uma representação gráfica para visualizar a perfomance de um modelo de classificação binária de acordo com a variação do seu liminar de discrição (Rate de True Positives Vs Rate de False Positives). Além da visualização, podemos calcular também a área embaixo da curva (ROC AUC) e transformar a métrica para uma forma numérica, facilitando as comparações entre diferentes modelos."},{"metadata":{},"cell_type":"markdown","source":"#### Desafio 05 - Plotar a curva ROC"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import plot_roc_curve, roc_auc_score\n\nplot_roc_curve(estimator = tree_model, X = X_test, y = y_test);","execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAA01UlEQVR4nO3deVxVdf7H8ddHFhFUXFgyURE30FJUTM3KBXOtcaaaykqzxkzLlmnam2mfpmb8Ne2WOVY6jbaX0z6aS1kmYOaGkrukCbiAiiDL5/fHvRIiwkW5HOB+no8HDzn3bO8DeD/3fM8536+oKsYYY3xXA6cDGGOMcZYVAmOM8XFWCIwxxsdZITDGGB9nhcAYY3ycv9MBqiosLEyjo6OdjmGMMXVKSkpKlqqGlzevzhWC6OhokpOTnY5hjDF1iohsP9k8axoyxhgfZ4XAGGN8nBUCY4zxcVYIjDHGx1khMMYYH+e1QiAis0QkQ0TWnmS+iMhzIrJJRFaLSC9vZTHGGHNy3jwjeB0YUcH8kUAn99ckYLoXsxhjjDkJrxUCVV0K7KtgkTHAbHVZDjQTkVbeymOMMXWRqrIp4yAvL9nMt5uyvLIPJx8oaw3sLDWd7n5td9kFRWQSrrMG2rZtWyPhjDHGKQVFxSRt3ceC1AwWbtjD9r25AEwZ1IFzO4ZV+/6cLARSzmvljpKjqjOAGQAJCQk2ko4xpt45kHuUxRszWZC6hyVpmRzMKyTQvwHndmjJxPNjSIyN4MxmjbyybycLQTrQptR0FLDLoSzGGFPjNmceYmHqHhakZpCyfT9FxUpY40BGnnUGiXGRnNcxjJCG3n+bdrIQzAemisg8oC+QraonNAsZY0x9UVhUTNK2/SxM3cPCDRlszToMQOwZTZgysAOJcRH0iGpGgwblNZh4j9cKgYjMBQYBYSKSDjwEBACo6svAp8AoYBOQC1znrSzGGOOU7NwCFqdlsDA1g8UbM8jJKyTQrwH9OrTkugHRDImNIKp5sKMZvVYIVHVsJfMVuNlb+zfGGKdszTrsbvLZQ9I2V5NPy5BAhnU7g6FxEZzXKZzGNdDk46nak8QYY+qowqJiUrbvZ+GGDBak7mFLpqvJp0tkE268IIbEuEji2zTDr4abfDxlhcAYY05BTl4BSzZmsjB1D4vTMjmQW0CAn9AvpiXj+7UjMS6SNi2cbfLxlBUCY4zx0Pa9h1339qfuYcXWfRQWK82DAxgSG8HQuEjO7xRGk6AAp2NWmRUCY4w5iaJiZeWO/SxI3cPC1Aw2ZRwCoFNEYyaeH8PQuAh6tm1ea5t8PGWFwBhjSjmYV8DStCwWpu5h0cYM9ucW4N9A6BvTgqvOacvQuEjatqwbTT6eskJgjPF5O/fllnzq/37rXgqKlGbBAQzuEkFiXAQXdA6naR1s8vGUFQJjjM8pKlZW7dxf0t6ftsfV5NMhPITrB7QnMS6SXm2b4e/nG0O2WCEwxviEQ/mFfJ2WyYLUDBZtzGDf4aP4NxD6RLfgz6PbMDQukuiwEKdjOsIKgTGm3krfn8vCVNe9/d9v2cfRomJCGwUwqEs4iXGRDOwcTmij+tvk4ykrBMaYeqO4WFmVfsDVl09qBht+OQhATFgI157rurc/oV1zn2ny8ZQVAmNMnXY4v5Cvf/r1Lp+sQ0fxayAktGvOA6PiSIyLICa8sdMxazUrBMaYOufnA0f4yt1983eb93K0qJgmQf4M6hLB0LgIBnYOp1lwoNMx6wwrBMaYWq+4WFn9c3ZJ3/2pu3MAiG4ZzLj+7UiMi6BPdAsCrMnnlFghMMbUSrlHf23y+WpDJlmH8mkgkNCuBfeNjCUxLpIO4SGI1O2nemsDKwTGmFpjd/YRFrrv7V+2eS9HC4tp0tCfC7qEMzQugkGdI2geYk0+1c0KgTHGMcXFytpd2SUPdq3b5WryadsimKv7urpz6BPdgkB/a/LxJisExpgadeRoEcs2ZbFwg+sWz4yDriafXm2bc8+IWIbGRdAxorE1+dQgKwTGGK/7JTuPhRv28FVqBt9syiK/sJjGDf25oHMYibGRDI6NoIU1+TjGCoExptqpKmt/znF15LZhD2t/djX5RDVvxNhz2pIYF0Hf9i2tyaeWsEJgjKkWeQWuJp8FqRl8tWEPe3LyEYGebZpx1/AuDI2LpHOkNfnURlYIjDGnLCMnj4UbXBd6v9mURV5BMSGBflzQOZwhsREMjo0grHFDp2OaSlghMMZ4TFVZtyvHdYvnhj2sTs8GoHWzRlyR0IbEuEj6xrSgob+fw0lNVVghMMZUKK+giO8272VB6h6+2pDB7uw8RCDe3eSTGBdBl8gm1uRTh1khMMacIONgHos2ZLAgNYNvfsriSEERwYF+nN8pjD9e2JnBXSIIb2JNPvWFFQJjDKpK6u6Drr58NmTw484DAJwZGsSlvVszNC6SfjEtCQqwJp/6yAqBMT4qv9DV5LMwNYOvNmTw84EjAPRo04w/XdiZxLhI4lpZk48vsEJgjA/JOpTPV+67fL7+KYvco0U0CvDjvE5h3JrYkcGxEUQ0CXI6pqlhVgiMqcdUlY17DpYM17hq5wFU4YymQfyup6vJp38Ha/LxdVYIjKln8guL+H7LPtdwjRsySN/vavLpHhXK7YmdSYyLoNuZTa3Jx5SwQmBMPbD3UD6LNmayMHUPS9MyOXy0iKCABpzXMYybB3dkSGwEkU2tyceUzwqBMXWQqvJTxiFXXz6pGazcsR9ViGzakN/Et2ZoXATndgijUaA1+ZjKebUQiMgI4FnAD5ipqk+WmR8K/Bto684yTVVf82YmY+qqo4XFrNi6r6Qjt537XE0+Z7Vuyq1DOjE0LpKzWluTj6k6rxUCEfEDXgQuBNKBJBGZr6rrSy12M7BeVS8WkXBgo4i8qapHvZXLmLpk/+GjLNqYwcLUDJamZXIwv5CG/g0Y0DGMyQM7kBgbyRmh1uRjTo83zwjOATap6hYAEZkHjAFKFwIFmojrI0xjYB9Q6MVMxtRqqsrmzEMlI3albN9PsUJ4k4aM7t6KxLhIBnRsSXCgteqa6uPNv6bWwM5S0+lA3zLLvADMB3YBTYArVLW47IZEZBIwCaBt27ZeCWuMUwqKiknaus/15r9hD9v35gLQtVVTpg7uSGJcJGe3DqVBA2vyMd7hzUJQ3l+tlpkeDqwChgAdgP+JyNeqmnPcSqozgBkACQkJZbdhTJ1zIPcoizdmsiB1D0vSMjmYV0igfwPO7dCSiefHkBgbwZnNGjkd0/gIbxaCdKBNqekoXJ/8S7sOeFJVFdgkIluBWGCFF3MZ44jNmYdcffmkZpCyfT9FxUpY40BGnnUGiXGRnNcxjJCG1uRjap43/+qSgE4i0h74GbgSuKrMMjuAROBrEYkEugBbvJjJmBpTUFRM8rb9JQ92bc06DEDsGU2YMrADiXER9IhqZk0+xnFeKwSqWigiU4EvcN0+OktV14nIZPf8l4HHgNdFZA2upqR7VDXLW5mM8bbs3AIWp7nu8lm8MYOcvEIC/RrQr0NLrhsQzZDYCKKaBzsd05jjePU8VFU/BT4t89rLpb7fBQzzZgZjvG1r1mF3k88ekra5mnxahgQyrNsZDI2L4LxO4TS2Jh9Ti9lfpzFVVFhUTMr2/Szc4OrIbUumq8mnS2QTbrwghsS4SOLbNMPPmnxMHWGFwBgP5OQVsMTdl8+ijZlkHykgwE/oF9OS8f3akRgXSZsW1uRj6iYrBMacxLaswyx0992/Yus+CouV5sEBJMZFMDQukvM7hdEkKMDpmMacNisExrgVFhWzcseBkvb+ze4mn04RjZl4fgxD4yLo2ba5NfmYescKgfFpOXkFLE3LZGFqBos2ZnAgtwD/BkLfmBZc3bcdQ+MiadvSmnxM/WaFwPicnftyWeD+1L9i6z4KipRmwQEM7hJBYlwEF3QOp6k1+RgfYoXA1HtFxcqqnftLOnJL23MIgA7hIVw/oD2JcZH0atsMf78GDic1xhlWCEy9dCi/kKVprr58Fm/MZN/ho/g3EPpEt+DPo9swNC6S6LAQp2MaUytYITD1xs59uSXdOSzfspeCIiW0UQCDuoSTGBfJwM7hhDayJh9jyrJCYOosV5OP6y6fhakZbNxzEICYsBAmnBtNYlwkCe2aW5OPMZXwuBCISIiqHvZmGGM8lVdQxLh/fU/Stv34NRAS2jXngVFxJMZFEBPe2Ol4xtQplRYCETkXmIlrBLG2ItIDuFFVb/J2OGPKo6rc9/4akrbt56GLu3JJzyhCg63Jx5hT5ck58z9xDSCzF0BVfwQu8GYoYyryytItfPDDz9xxYWeuG9DeioAxp8mjxlNV3VnmpSIvZDGmUgtT9/DU5xsY3b0Vtwzp6HQcY+oFT64R7HQ3D6mIBAK3AqnejWXMidL2HOTWuT/Q7cymTLusByLW1YMx1cGTM4LJwM24BqNPB+IBuz5gatT+w0eZ+EYywQ39eXV8Ao0C/ZyOZEy94ckZQRdVvbr0CyIyAFjmnUjGHK+gqJgpb6bwS04e8yb1o1WoDepuTHXy5IzgeQ9fM8YrHp6/juVb9vHkJWfTq21zp+MYU++c9IxARPoD5wLhInJHqVlNcY1BbIzXzfluG29+v4MbB8ZwSa8op+MYUy9V1DQUiOvZAX+gSanXc4DLvBnKGIBvN2Xx8H/XMyQ2gruHxzodx5h666SFQFWXAEtE5HVV3V6DmYxhW9Zhpry5kpiwEJ69Mt4GgzHGizy5WJwrIv8AugFBx15U1SFeS2V8Wk5eARNnJyMC/7q2jw0HaYyXeXKx+E1gA9AeeATYBiR5MZPxYUXFym1zf2Bb1mFeurqXjQ5mTA3wpBC0VNV/AQWqukRVrwf6eTmX8VFPfb6BRRszefg33Ti3Q5jTcYzxCZ40DRW4/90tIqOBXYDdvmGq3bsp6cxYuoVx/dpxTb92Tscxxmd4UggeF5FQ4E+4nh9oCtzuzVDG96Rs38/976+hf0xLHry4q9NxjPEplRYCVf3Y/W02MBhKniw2plrsOnCEG+ek0KpZEC9d3YsAG0jGmBpV0QNlfsDluPoY+lxV14rIRcD9QCOgZ81ENPVZ7tFCbpidTF5BEXNv6EvzkECnIxnjcyo6I/gX0AZYATwnItuB/sC9qvphDWQz9VxxsXLnOz+yfncOs67tQ6fIJpWvZIypdhUVggSgu6oWi0gQkAV0VNVfaiaaqe+e++onPl3zC/ePimVwbITTcYzxWRU1xh5V1WIAVc0D0qpaBERkhIhsFJFNInLvSZYZJCKrRGSdiCypyvZN3fXpmt08s+AnLunVmhvOj3E6jjE+raIzglgRWe3+XoAO7mkBVFW7V7Rh9zWGF4ELcY1jkCQi81V1fallmgEvASNUdYeI2MdCH7D252zueHsVPds244nfnW0DzBjjsIoKQdxpbvscYJOqbgEQkXnAGGB9qWWuAt5X1R0Aqppxmvs0tVzmwXwmzU6meXAgr4zrTVCAdWRrjNMq6nTudDuaaw2UHus4HehbZpnOQICILMbVw+mzqjq77IZEZBIwCaBt27anGcs4Jb+wiBvnJLMv9yjvTj6XiCZBla9kjPE6b96wXd75vpaZ9gd6A6OB4cBfRKTzCSupzlDVBFVNCA8Pr/6kxutUlfvfX8vKHQf4v9/Hc1brUKcjGWPcPHmy+FSl47r99JgoXN1TlF0mS1UPA4dFZCnQA0jzYi7jgJlfb+W9lencltiJ0d1bOR3HGFOKR2cEItJIRLpUcdtJQCcRaS8igcCVwPwyy3wEnC8i/iISjKvpKLWK+zG13KINGfzts1RGnnUGtyV2cjqOMaaMSguBiFwMrAI+d0/Hi0jZN/QTqGohMBX4Ateb+9uquk5EJovIZPcyqe7trsb14NpMVV17isdiaqFNGQe5de4PxJ7RlP+7vAcNbIAZY2odUS3bbF9mAZEUYAiwWFV7ul9bXdnto96SkJCgycnJTuzaVNGB3KOMeXEZh/ML+WjqebRu1sjpSMb4LBFJUdWE8uZ50jRUqKrZ1ZzJ1HMFRcXc/J+V7D6QxyvjelsRMKYW8+Ri8VoRuQrwE5FOwK3At96NZeq6xz5ez7JNe/nHZd3p3a6F03GMMRXw5IzgFlzjFecD/8HVHfXtXsxk6rg3v9/O7O+2c8P57fl9QpvKVzDGOMqTM4IuqvoA8IC3w5i677vNe3noo3UM6hLOvSNP9+F0Y0xN8OSM4GkR2SAij4lIN68nMnXWjr25THkzhXYtg3lubE/87A4hY+qESguBqg4GBgGZwAwRWSMif/Z2MFO3HMwrYOLsJFThX9f2oWlQgNORjDEe8uiBMlX9RVWfAybjeqbgQW+GMnVLUbFy+7xVbM48zEtX9yI6LMTpSMaYKvDkgbI4EXlYRNYCL+C6YyjK68lMnfGPLzaycEMGD13clQEdw5yOY4ypIk8uFr8GzAWGqWrZvoKMj/vgh3ReXrKZq/q2ZVy/dk7HMcacgkoLgar2q4kgpu75Ycd+7nlvDX3bt+CR33SzAWaMqaNOWghE5G1VvVxE1nB899EejVBm6rfd2UeYNCeFyKYNmX5NbwL8vNmjuTHGmyo6I7jN/e9FNRHE1B1HjhYxaXYKufmFvDlxAC1CAp2OZIw5DSf9GKequ93f3qSq20t/ATfVTDxT26gqd737I2t3ZfPslT3pHNnE6UjGmNPkyfn8heW8NrK6g5i64YWvNvHx6t3cPTyWoV0jnY5jjKkGFV0jmILrk3+MiKwuNasJsMzbwUzt8/naX/i//6Xxu56tmTwwxuk4xphqUtE1gv8AnwF/A+4t9fpBVd3n1VSm1lm/K4c73l5FjzbN+NslZ9sdQsbUIxUVAlXVbSJyc9kZItLCioHvyDqUzw2zk2kaFMCr43oTFODndCRjTDWq7IzgIiAF1+2jpT8CKmBtAz7gaGExU/6dQtahfN6Z3J+IpkFORzLGVLOTFgJVvcj9b/uai2NqE1Xlzx+uIWnbfp4f25PuUc2cjmSM8QJP+hoaICIh7u+vEZGnRaSt96MZp81ato23k9O5ZUhHLu5xptNxjDFe4snto9OBXBHpAdwNbAfmeDWVcdyStEz++sl6hneL5I9DOzsdxxjjRZ4OXq/AGOBZVX0W1y2kpp7anHmIqf9ZSefIJjx9eTwNbIAZY+o1T3ofPSgi9wHjgPNFxA+wUUfqqezcAia+kUygXwNmXptASENP/kSMMXWZJ2cEV+AauP56Vf0FaA38w6upjCMKi4qZOncl6ftzeXlcb6KaBzsdyRhTAzwZqvIX4E0gVEQuAvJUdbbXk5ka9/gnqXz9UxaP//Ys+kS3cDqOMaaGeHLX0OXACuD3wOXA9yJymbeDmZo1b8UOXv92G9cPaM8VfeymMGN8iScNwA8AfVQ1A0BEwoEFwLveDGZqzvdb9vKXj9ZyQedw7h8V63QcY0wN8+QaQYNjRcBtr4frmTpg575cpry5kjbNg3l+bE/8bYAZY3yOJ2cEn4vIF7jGLQbXxeNPvRfJ1JRD+YXcMDuZwqJiZl6bQGgjuxnMGF/kyZjFd4nIJcB5uPobmqGqH3g9mfGq4mLlj2+tIm3PQV6/7hxiwhs7HckY45CKxiPoBEwDOgBrgDtV9eeaCma86//+t5H/rd/DQxd35YLO4U7HMcY4qKIG4VnAx8CluHogfb6qGxeRESKyUUQ2ici9FSzXR0SK7G6kmvHRqp95cdFmruzThgnnRjsdxxjjsIqahpqo6qvu7zeKyMqqbNj9BPKLuIa6TAeSRGS+qq4vZ7mngC+qsn1zan7ceYC7313NOdEteHTMWTbAjDGmwkIQJCI9+XUcgkalp1W1ssJwDrBJVbcAiMg8XP0VrS+z3C3Ae0CfKmY3VbQnJ48bZicT1rgh06/pRaC/3SFkjKm4EOwGni41/UupaQWGVLLt1sDOUtPpQN/SC4hIa+B37m2dtBCIyCRgEkDbtvaw06nIKyhi0uxkDuUX8t6Uc2nZuKHTkYwxtURFA9MMPs1tl9fmoGWmnwHuUdWiipooVHUGMAMgISGh7DZMJVSVe95bzY/p2bwyrjdxrZo6HckYU4t4s2vJdKBNqekoYFeZZRKAee4iEAaMEpFCVf3Qi7l8zkuLN/PRql3cNbwLw7ud4XQcY0wt481CkAR0EpH2wM/AlcBVpRcoPQymiLwOfGxFoHr9b/0epn25kd/0OJObBnVwOo4xphbyWiFQ1UIRmYrrbiA/YJaqrhORye75L3tr38Zlwy853D7vB85uHcrfL+tudwgZY8pVaSEQ17vH1UCMqj7qHq/4DFVdUdm6qvopZbqjOFkBUNUJHiU2Htl7KJ+JbyQT0tCfGeMSCArwczqSMaaW8uT+wZeA/sBY9/RBXM8HmFrqaGExU95cScbBfGaMT+CM0CCnIxljajFPmob6qmovEfkBQFX3i0igl3OZU6SqPDR/LSu27uPZK+OJb9PM6UjGmFrOkzOCAvfTvwol4xEUezWVOWVvfLuNuSt2ctOgDoyJb+10HGNMHeBJIXgO+ACIEJG/At8AT3g1lTkl3/yUxWOfpDI0LpI7h3VxOo4xpo7wpBvqN0UkBUjE9ZDYb1U11evJTJVszTrMTW+m0DG8Mc9cGU+DBnaHkDHGM57cNdQWyAX+W/o1Vd3hzWDGc9lHCvjDG0n4NRBmXptA44befDzEGFPfePKO8Qmu6wMCBAHtgY1ANy/mMh4qKlZunfsDO/bm8u+JfWnTItjpSMaYOsaTpqGzS0+LSC/gRq8lMlXyt09TWZKWyRO/O5t+MS2djmOMqYOq3A+xu/tp6zK6Fng7aSczv9nKhHOjuaqv9cpqjDk1nlwjuKPUZAOgF5DptUTGI8nb9vHAh2s4r2MYfx4d53QcY0wd5sk1gialvi/Edc3gPe/EMZ5I35/LjXNSiGoezItX9cLfzwaYMcacugoLgftBssaqelcN5TGVOJxfyA2zUzhaVMyr4xMIDQ5wOpIxpo476UdJEfFX1SJcTUGmFiguVv709o9s/CWH58f2pGNEY6cjGWPqgYrOCFbgKgKrRGQ+8A5w+NhMVX3fy9lMGc8sSOPzdb/w59FxDOoS4XQcY0w94ck1ghbAXlzjCh97nkABKwQ16OPVu3juq038vncUfzivfeUrGGOMhyoqBBHuO4bW8msBOMbGDa5Ba9KzufOdH+ndrjmP/+4sG2DGGFOtKioEfkBjPBuE3nhJRk4eN8xOpmVIQ16+pjcN/W2AGWNM9aqoEOxW1UdrLIk5QV5BEZPmpJB9pIB3p/QnvElDpyMZY+qhigqBtT84SFW5//01rNp5gJev6UW3M0OdjmSMqacqehIpscZSmBO8snQL7//wM3dc2JkRZ7VyOo4xph47aSFQ1X01GcT8amHqHp76fAOju7filiEdnY5jjKnnrG+CWiZtz0Fum7eKbmc2ZdplPewOIWOM11khqEX2Hz7KxDeSaRTox6vjE2gUaHcIGWO8z4ayqiUKioqZ8mYKv+TkMW9SP1qFNnI6kjHGR9gZQS3xyH/XsXzLPp685Gx6tW3udBxjjA+xQlALzPluG/9evoMbB8ZwSa8op+MYY3yMFQKHfbspi4f/u54hsRHcPTzW6TjGGB9khcBB27IOM+XNlcSEhfDslfH4NbA7hIwxNc8KgUNy8gqYODsZEZh5bQJNgmyAGWOMM6wQOKCoWLlt7g9syzrMS1f3ol3LEKcjGWN8mFcLgYiMEJGNIrJJRO4tZ/7VIrLa/fWtiPTwZp7a4u+fb2DRxkwe+k03zu0Q5nQcY4yP81ohcI93/CIwEugKjBWRrmUW2woMVNXuwGPADG/lqS3eTUnnlaVbGNevHeP6tXM6jjHGePWM4Bxgk6puUdWjwDxgTOkFVPVbVd3vnlwO1Ot7J1O27+f+99fQP6YlD15ctiYaY4wzvFkIWgM7S02nu187mT8An5U3Q0QmiUiyiCRnZmZWY8Sas+vAEW6ck0KrZkG8dHUvAvzs8owxpnbw5ruRxyObichgXIXgnvLmq+oMVU1Q1YTw8PBqjFgzco8WcsPsZPIKipg5PoHmIYFORzLGmBLe7GsoHWhTajoK2FV2IRHpDswERqrqXi/mcYSqctc7q1m/O4dZ1/ahU2QTpyMZY8xxvHlGkAR0EpH2IhIIXAnML72AiLQF3gfGqWqaF7M45rmFm/hkzW7uGxnL4NgIp+MYY8wJvHZGoKqFIjIV+ALwA2ap6joRmeye/zLwINASeMnd736hqiZ4K1NN+2zNbv65II1LerXmhvNjnI5jjDHlEtVym+1rrYSEBE1OTnY6RqXW7crmsunfEduqCXNv6EdQgI0tYIxxjoiknOyDtt264gWZB/O54Y1kmgUH8Mq43lYEjDG1mg1MU83yC4u4cU4y+3KP8u7kc4loEuR0JGOMqZAVgmqkqjzwwVpW7jjAi1f14qzWoU5HMsaYSlnTUDWa+fVW3k1J57bETozu3srpOMYY4xErBNVk0cYM/vZZKiPPOoPbEjs5HccYYzxmhaAabMo4yK3/+YHYM5ryf5f3oIENMGOMqUOsEJymA7lH+cMbyTQMaMCr1yYQHGiXXYwxdYu9a52GgqJibv7PSnYfyGPupL60btbI6UjGGFNlVghOw+Mfr2fZpr3847Lu9G7Xwuk4xhhzSqxp6BS9+f123vhuOzec357fJ7SpfAVjjKmlrBCcgu827+Whj9YxqEs4946MczqOMcacFisEVbRjby43vZlCu5bBPDe2J352h5Axpo6zQlAFB/MKmDg7iWKFmdf2oWlQgNORjDHmtNnFYg8VFSt/fGsVmzMPM/v6c2gfFuJ0JGOMqRZ2RuChaV9uZEFqBg9e1JUBHcOcjmOMMdXGCoEHPvghnemLN3NV37aM79/O6TjGGFOtrBBU4ocd+7nnvTX0bd+CR37TDfdIasYYU29YIajA7uwjTJqTQmTThky/pjcBfvbjMsbUP/bOdhJHjhYxaXYKufmFzBzfhxYhgU5HMsYYr7C7hsqhqtz93mrW7srm1XEJdDmjidORjDHGa+yMoBwvLtrEf3/cxV3DuzC0a6TTcYwxxqvsjKCMz9f+wrQv0/ht/JlMGdjB6Tj1XkFBAenp6eTl5TkdxZh6ISgoiKioKAICPH/g1QpBKam7c7jj7VX0aNOMJy/tbncI1YD09HSaNGlCdHS0/byNOU2qyt69e0lPT6d9+/Yer2dNQ25Zh/KZ+EYyTYL8eXVcb4IC/JyO5BPy8vJo2bKlFQFjqoGI0LJlyyqfYdsZAXC0sJgp/04h61A+70zuT0TTIKcj+RQrAsZUn1P5/+TzhUBV+cuHa0natp/nx/ake1QzpyMZY0yN8vmmodeWbeOt5J3cMqQjF/c40+k4xgF+fn7Ex8fTrVs3evTowdNPP01xcfEpbevBBx9kwYIFJ53/8ssvM3v27Cpv94svviA+Pp74+HgaN25Mly5diI+PZ/z48aeUs7Rp06YRGxvLWWedRY8ePUryDRo0iOTk5NPePkBycjK33norAPn5+QwdOpT4+HjeeustJk6cyPr1609r+88888xxP9fCwkLCwsK47777jlsuOjqarKyskunFixdz0UUXlUx/9tlnJCQkEBcXR2xsLHfeeedp5QJISUnh7LPPpmPHjtx6662oarnLrV69mv79+9OtWzfOPvts8vLyOHjwYMnvPT4+nrCwMG6//XYAXnjhBV577bXTzge4PhHXpa/evXtrdVm8MUPb3/uxTpqdpEVFxdW2XeO59evXOx1BQ0JCSr7fs2ePJiYm6oMPPuhgoooNHDhQk5KSTni9sLCwytuaPn26Dhs2TLOzs1VV9cCBA/r6669XuJ/T9d133+kFF1xwyuuXPc6CggI9++yztaCgoOS1Tz75RM8991yNiYnR4uJf/2+3a9dOMzMzS6YXLVqko0ePVlXVNWvWaExMjKamppZs98UXXzzlnMf06dNHv/32Wy0uLtYRI0bop59+esIyx45h1apVqqqalZVV7u+zV69eumTJElVVPXz4sMbHx5e7z/L+XwHJepL3VZ9tGtqceYip/1lJ58gmPH15PA1sgBnHPfLfdazflVOt2+x6ZlMeuribx8tHREQwY8YM+vTpw8MPP0xxcTH33nsvixcvJj8/n5tvvpkbb7wRgL///e/MmTOHBg0aMHLkSJ588kkmTJjARRddxGWXXca9997L/Pnz8ff3Z9iwYUybNo2HH36Yxo0bc+edd7Jq1SomT55Mbm4uHTp0YNasWTRv3pxBgwbRt29fFi1axIEDB/jXv/7F+eefX27e6Ohorr/+er788kumTp1KixYteOihh8jPz6dDhw689tprNG7cmJSUFO644w4OHTpEWFgYr7/+Oq1ateKJJ55g0aJFNG3aFIDQ0FCuvfbaE/YzZcoUkpKSOHLkCJdddhmPPPIIQLnH+M477/DII4/g5+dHaGgoS5cuZfHixUybNo1Zs2ZxzTXXkJmZSXx8PO+99x5/+MMfmDZtGgkJCXz55Zfl5i97nFdeeWVJtq+++opevXrh7//r29ncuXO57bbbmD59OsuXL6d///6V/u7//ve/88ADDxAbGwuAv78/N910k4d/OeXbvXs3OTk5JfsfP348H374ISNHjjxuuS+//JLu3bvTo0cPAFq2bHnCtn766ScyMjJK/haCg4OJjo5mxYoVnHPOOaeV0ycLQXZuATe8kUygXwNmXptASEOf/DGYk4iJiaG4uJiMjAw++ugjQkNDSUpKIj8/nwEDBjBs2DA2bNjAhx9+yPfff09wcDD79u07bhv79u3jgw8+YMOGDYgIBw4cOGE/48eP5/nnn2fgwIE8+OCDPPLIIzzzzDOAq2ljxYoVfPrppzzyyCMVNjcFBQXxzTffkJWVxSWXXMKCBQsICQnhqaee4umnn+a+++7jlltu4aOPPiI8PJy33nqLBx54gGeffZaDBw/SoUPlz8v89a9/pUWLFhQVFZGYmMjq1auJiooq9xgfffRRvvjiC1q3bn3CcUdERDBz5kymTZvGxx9/fNy8rKwsHn/88RPyP/jgg8cdZ1nLli2jd+/eJdNHjhxh4cKFvPLKKxw4cIC5c+d6VAjWrl3Ln/70p0qXW7RoEX/84x9PeD04OJhvv/32uNd+/vlnoqKiSqajoqL4+eefT1g3LS0NEWH48OFkZmZy5ZVXcvfddx+3zNy5c7niiiuOuxickJDA119/bYWgqgqLipk6dyU79+fynxv6EdU82OlIxq0qn9y9Td3tuF9++SWrV6/m3XffBSA7O5uffvqJBQsWcN111xEc7Pr7adGixXHrN23alKCgICZOnMjo0aOPa4c+tp0DBw4wcOBAAK699lp+//vfl8y/5JJLAOjduzfbtm2rMOsVV1wBwPLly1m/fj0DBgwA4OjRo/Tv35+NGzeydu1aLrzwQgCKiopo1aoVqurxHSZvv/02M2bMoLCwkN27d7N+/Xq6du1a7jEOGDCACRMmcPnll5cchydOlr/scZa1e/du4uJ+HTv8448/ZvDgwQQHB3PppZfy2GOP8c9//hM/P79yj7eqd9kMHjyYVatWebTssb+jyvZXWFjIN998Q1JSEsHBwSQmJtK7d28SExNLlpk3bx5z5sw5br2IiAg2bNhQpfzl8WohEJERwLOAHzBTVZ8sM1/c80cBucAEVV3pzUx//TSVr3/K4qlLz6ZPdIvKVzA+Z8uWLfj5+REREYGq8vzzzzN8+PDjlvn8888rfAPx9/dnxYoVLFy4kHnz5vHCCy/w1VdfeZyhYcOGgOtCdmFhYYXLhoS4RstTVS688ELmzp173Pw1a9bQrVs3vvvuu3LX3bJlCzExMSfd/tatW5k2bRpJSUk0b96cCRMmkJeXd9JjfPnll/n+++/55JNPiI+Pr9KbZnn5yx5nWY0aNTruvvm5c+eybNkyoqOjAdi7dy+LFi1i6NChtGzZkv379xMW5hpcat++fSXfd+vWjZSUlJLmmZOpyhlBVFQU6enpJdPp6emceeaJN6VERUUxcODAkiyjRo1i5cqVJYXgxx9/pLCw8LgzH3A9h9OoUaMK83rCa3cNiYgf8CIwEugKjBWRrmUWGwl0cn9NAqZ7Kw/AvBU7eG3ZNq4f0J4r+rT15q5MHZWZmcnkyZOZOnVqyan69OnTKSgoAFyn8IcPH2bYsGHMmjWL3NxcgBOahg4dOkR2djajRo3imWeeOeHNMDQ0lObNm/P1118DMGfOnJKzg1PVr18/li1bxqZNmwDIzc0lLS2NLl26kJmZWVIICgoKWLduHQD33XcfN998Mzk5rmszOTk5zJgx47jt5uTkEBISQmhoKHv27OGzzz6r8Bg3b95M3759efTRRwkLC2Pnzp2nlb8ycXFxJevk5OTwzTffsGPHDrZt28a2bdt48cUXS4rLoEGDSj5VFxUV8e9//5vBgwcDcNddd/HEE0+U7LO4uJinn376hP0dOyMo+1W2CAC0atWKJk2asHz5clSV2bNnM2bMmBOWGz58OKtXryY3N5fCwkKWLFlC166/vl3OnTuXsWPHnrBeWloaZ511VqU/o8p484zgHGCTqm4BEJF5wBig9H1iY4DZ7ivay0WkmYi0UtXd1R1mxdZ9/OWjtVzQOZz7R8VW9+ZNHXbkyBHi4+MpKCjA39+fcePGcccddwAwceJEtm3bRq9evVBVwsPD+fDDDxkxYgSrVq0iISGBwMBARo0axRNPPFGyzYMHDzJmzBjy8vJQVf75z3+esN833nij5GJxTEzMad8KGB4ezuuvv87YsWPJz88H4PHHH6dz5868++673HrrrWRnZ1NYWMjtt99Ot27dmDJlCocOHaJPnz4EBAQQEBBwQjt5jx496NmzJ926dSMmJqak6eZkx3jXXXfx008/oaokJibSo0cPlixZclr5KzJy5EjGjRsHwPvvv8+QIUNKzqgAxowZw913301+fj5/+ctfmDJlCj169EBVGTFiBNdccw0A3bt355lnnmHs2LHk5uYiIowePdqTH32Fpk+fzoQJEzhy5AgjR44suVA8f/58kpOTefTRR2nevDl33HEHffr0QUQYNWrUcft+++23+fTTT0/Y9rJly3jooYdOO6PXbvMELsPVHHRsehzwQpllPgbOKzW9EEgoZ1uTgGQguW3btuXeLlWZ9buy9ZqZy/VA7tFTWt94R224fdTUfb/97W81LS3N6Rg1auXKlXrNNdeUO6+qt49684Gy8hpQy1458WQZVHWGqiaoakJ4ePgphYlr1ZQ5f+hLaCPPe+QzxtQNTz75JLt3V3tDQq2WlZXFY489Vi3b8mbTUDrQptR0FLDrFJYxxpgKdenShS5dujgdo0YduwusOnjzjCAJ6CQi7UUkELgSmF9mmfnAeHHpB2SrF64PmNpNT/LIvTGm6k7l/5PXzghUtVBEpgJf4Lp9dJaqrhORye75LwOf4rp1dBOu20ev81YeUzsFBQWxd+9e64ramGqg7vEIgoKq1oOy1LVPYwkJCVpdHWEZ59kIZcZUr5ONUCYiKaqaUN46PvdksaldAgICqjSSkjGm+vl8N9TGGOPrrBAYY4yPs0JgjDE+rs5dLBaRTGD7Ka4eBmRVulT9YsfsG+yYfcPpHHM7VS33idw6VwhOh4gkn+yqeX1lx+wb7Jh9g7eO2ZqGjDHGx1khMMYYH+drhWBG5YvUO3bMvsGO2Td45Zh96hqBMcaYE/naGYExxpgyrBAYY4yPq5eFQERGiMhGEdkkIveWM19E5Dn3/NUi0suJnNXJg2O+2n2sq0XkWxGpeITuOqCyYy61XB8RKRKRy2oynzd4cswiMkhEVonIOhGpfIzIWs6Dv+1QEfmviPzoPuY63YuxiMwSkQwRWXuS+dX//nWyocvq6heuLq83AzFAIPAj0LXMMqOAz3CNkNYP+N7p3DVwzOcCzd3fj/SFYy613Fe4ujy/zOncNfB7boZrXPC27ukIp3PXwDHfDzzl/j4c2AcEOp39NI75AqAXsPYk86v9/as+nhGcA2xS1S2qehSYB4wps8wYYLa6LAeaiUirmg5ajSo9ZlX9VlX3uyeX4xoNri7z5PcMcAvwHpBRk+G8xJNjvgp4X1V3AKhqXT9uT45ZgSbiGtCiMa5CUFizMauPqi7FdQwnU+3vX/WxELQGdpaaTne/VtVl6pKqHs8fcH2iqMsqPWYRaQ38Dni5BnN5kye/585AcxFZLCIpIjK+xtJ5hyfH/AIQh2uY2zXAbapaXDPxHFHt71/1cTyC8oa5KnuPrCfL1CUeH4+IDMZVCM7zaiLv8+SYnwHuUdWiejL6mSfH7A/0BhKBRsB3IrJcVdO8Hc5LPDnm4cAqYAjQAfifiHytqjlezuaUan//qo+FIB1oU2o6CtcnhaouU5d4dDwi0h2YCYxU1b01lM1bPDnmBGCeuwiEAaNEpFBVP6yRhNXP07/tLFU9DBwWkaVAD6CuFgJPjvk64El1NaBvEpGtQCywomYi1rhqf/+qj01DSUAnEWkvIoHAlcD8MsvMB8a7r773A7JVdXdNB61GlR6ziLQF3gfG1eFPh6VVesyq2l5Vo1U1GngXuKkOFwHw7G/7I+B8EfEXkWCgL5BawzmrkyfHvAPXGRAiEgl0AbbUaMqaVe3vX/XujEBVC0VkKvAFrjsOZqnqOhGZ7J7/Mq47SEYBm4BcXJ8o6iwPj/lBoCXwkvsTcqHW4Z4bPTzmesWTY1bVVBH5HFgNFAMzVbXc2xDrAg9/z48Br4vIGlzNJveoap3tnlpE5gKDgDARSQceAgLAe+9f1sWEMcb4uPrYNGSMMaYKrBAYY4yPs0JgjDE+zgqBMcb4OCsExhjj46wQmFrJ3VvoqlJf0RUse6ga9ve6iGx172uliPQ/hW3MFJGu7u/vLzPv29PN6N7OsZ/LWnePm80qWT5eREZVx75N/WW3j5paSUQOqWrj6l62gm28Dnysqu+KyDBgmqp2P43tnXamyrYrIm8Aaar61wqWnwAkqOrU6s5i6g87IzB1gog0FpGF7k/ra0TkhJ5GRaSViCwt9Yn5fPfrw0TkO/e674hIZW/QS4GO7nXvcG9rrYjc7n4tREQ+cfd/v1ZErnC/vlhEEkTkSaCRO8eb7nmH3P++VfoTuvtM5FIR8RORf4hIkrj6mL/Rgx/Ld7g7GxORc8Q1zsQP7n+7uJ/EfRS4wp3lCnf2We79/FDez9H4IKf73rYv+yrvCyjC1ZHYKuADXE/BN3XPC8P1VOWxM9pD7n//BDzg/t4PaOJedikQ4n79HuDBcvb3Ou7xCoDfA9/j6rxtDRCCq3vjdUBP4FLg1VLrhrr/XYzr03dJplLLHMv4O+AN9/eBuHqRbARMAv7sfr0hkAy0LyfnoVLH9w4wwj3dFPB3fz8UeM/9/QTghVLrPwFc4/6+Ga4+iEKc/n3bl7Nf9a6LCVNvHFHV+GMTIhIAPCEiF+DqOqE1EAn8UmqdJGCWe9kPVXWViAwEugLL3F1rBOL6JF2ef4jIn4FMXD20JgIfqKsDN0TkfeB84HNgmog8has56esqHNdnwHMi0hAYASxV1SPu5qju8usoaqFAJ2BrmfUbicgqIBpIAf5Xavk3RKQTrp4oA06y/2HAb0TkTvd0ENCWut0fkTlNVghMXXE1rtGneqtqgYhsw/UmVkJVl7oLxWhgjoj8A9gP/E9Vx3qwj7tU9d1jEyIytLyFVDVNRHrj6u/lbyLypao+6slBqGqeiCzG1XXyFcDcY7sDblHVLyrZxBFVjReRUOBj4GbgOVz97SxS1d+5L6wvPsn6Alyqqhs9yWt8g10jMHVFKJDhLgKDgXZlFxCRdu5lXgX+hWu4v+XAABE51uYfLCKdPdznUuC37nVCcDXrfC0iZwK5qvpvYJp7P2UVuM9MyjMPV0dh5+PqTA33v1OOrSMind37LJeqZgO3Ane61wkFfnbPnlBq0YO4msiO+QK4RdynRyLS82T7ML7DCoGpK94EEkQkGdfZwYZylhkErBKRH3C14z+rqpm43hjnishqXIUh1pMdqupKXNcOVuC6ZjBTVX8AzgZWuJtoHgAeL2f1GcDqYxeLy/gS17i0C9Q1/CK4xolYD6wU16Dlr1DJGbs7y4+4umb+O66zk2W4rh8cswjoeuxiMa4zhwB3trXuaePj7PZRY4zxcXZGYIwxPs4KgTHG+DgrBMYY4+OsEBhjjI+zQmCMMT7OCoExxvg4KwTGGOPj/h9Sr8sKPsWncAAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"prob_tree = tree_model.predict_proba(X_test)\nroc_auc_score(y_test, prob_tree[:,1])","execution_count":22,"outputs":[{"output_type":"execute_result","execution_count":22,"data":{"text/plain":"0.6656534954407295"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Primeria função de avaliação de modelo\n\ndef roda_modelo(modelo, dados):\n    X = dados.drop([\"ICU\", \"WINDOW\"], axis = 1)\n    y = dados['ICU']\n    \n    X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y)\n    \n    modelo.fit(X_train, y_train)\n    predicao = modelo.predict(X_test)\n    \n    auc = roc_auc_score(y_test, modelo.predict_proba(X_test)[:,1])\n    print(f\"AUC: {auc}\")\n    print(\"\\nClassification Report \")\n    print(classification_report(y_test, predicao))","execution_count":23,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"roda_modelo(tree_model, df_limpo)","execution_count":24,"outputs":[{"output_type":"stream","text":"AUC: 0.5827181936604429\n\nClassification Report \n              precision    recall  f1-score   support\n\n           0       0.58      0.55      0.57        47\n           1       0.59      0.61      0.60        49\n\n    accuracy                           0.58        96\n   macro avg       0.58      0.58      0.58        96\nweighted avg       0.58      0.58      0.58        96\n\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"Cada vez que executamos a função acima, os resultados são diferentes. Isso ocorre pois tanto o algoritmo (Tree Decision) quanto o `train_test_split` tem caráter aleatório."},{"metadata":{},"cell_type":"markdown","source":"#### Desafio 06: Pensar e tentar diminuir o efeito da aleatoriedade nas previsões\n\n* Setar o random_state para um valor fixo (pode acabar beneficiando/prejudicando o modelo, mas facilita a parametrização).\n* Rodar o modelo diversas vezes com random_state variável e calcular a média e o desvio padrão dos resultados."},{"metadata":{},"cell_type":"markdown","source":"## Aula 03 - Aleatoriedade de Modelos"},{"metadata":{},"cell_type":"markdown","source":"Uma das formas de reduzir o efeito da aleatoriedade nos modelos é criar uma iteração que rode o modelo diversas vezes, armazenando cada um dos resultados. Em seguida, calcular a média, o desvio padrão e o intervalo de confiança de 2 desvios (95%).\n\nDessa forma, podemos ter maior confiança que o resultado obtido pelo modelo é real e não depende de sorte (ou azar)."},{"metadata":{"trusted":true},"cell_type":"code","source":"def roda_n_modelos(modelo, dados, n):\n    X = dados.drop([\"ICU\", \"WINDOW\"], axis = 1)\n    y = dados['ICU']\n    \n    auc_lista = []\n    \n    for i in range(n):\n        X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y)\n\n        modelo.fit(X_train, y_train)\n        \n        auc = roc_auc_score(y_test, modelo.predict_proba(X_test)[:,1])\n        auc_lista.append(auc)\n    \n    auc_medio = np.mean(auc_lista)\n    auc_std = np.std(auc_lista)\n    print(f\"AUC Médio: {auc_medio}\")\n    print(f\"AUC STD: {auc_std}\")\n    print(f\"LOW AUC: {auc_medio - 2*auc_std} - HIGH AUC: {auc_medio + 2*auc_std}\")","execution_count":25,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"roda_n_modelos(tree_model, df_limpo, 50)","execution_count":26,"outputs":[{"output_type":"stream","text":"AUC Médio: 0.6377941815023882\nAUC STD: 0.040994369676553934\nLOW AUC: 0.5558054421492803 - HIGH AUC: 0.7197829208554961\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"roda_n_modelos(lr_model, df_limpo, 50)","execution_count":27,"outputs":[{"output_type":"stream","text":"AUC Médio: 0.8022231871471993\nAUC STD: 0.041316056484228855\nLOW AUC: 0.7195910741787416 - HIGH AUC: 0.884855300115657\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Desafio 07: Estudar a documentação do *scikit-learn* e testar outros modelos utilizando o método acima."},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.ensemble import RandomForestClassifier\n\nmodels = [KNeighborsClassifier, GaussianNB, RandomForestClassifier]\n\nfor model in models:\n    inst_model = model()\n    print(f\"{model.__name__}\")\n    roda_n_modelos(inst_model, df_limpo, 20)\n    print(\"\")","execution_count":28,"outputs":[{"output_type":"stream","text":"KNeighborsClassifier\nAUC Médio: 0.6016934433347807\nAUC STD: 0.03609518965471963\nLOW AUC: 0.5295030640253414 - HIGH AUC: 0.67388382264422\n\nGaussianNB\nAUC Médio: 0.7652301346070343\nAUC STD: 0.059575721151318796\nLOW AUC: 0.6460786923043966 - HIGH AUC: 0.8843815769096719\n\nRandomForestClassifier\nAUC Médio: 0.7991315675206253\nAUC STD: 0.03627769491178584\nLOW AUC: 0.7265761776970536 - HIGH AUC: 0.871686957344197\n\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Aula 04 - Validação Cruzada"},{"metadata":{},"cell_type":"markdown","source":"Apesar de improvável (por causa do número de vezes que cada um dos modelos foi instanciado), instanciar o modelo como fizemos acima pode fazer o modelo deixe de treinar em alguma pedaço do dataset. O Cross-Validate força o modelo a treinar em todas as partes do dataset.\n\nLer mais em: https://scikit-learn.org/stable/modules/cross_validation.html\n\nPara evitar algum tipo de enviesamento no modelo, utilizamos o Cross-Validate em conjunto com algum tipo de splitter que aceite o uso de shuffle. No nosso caso, vamos utilizar o StratifiedKFold, que vai embaralhar e separar o dataset em `n_splits` partes e estratificar."},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import cross_validate\nfrom sklearn.model_selection import StratifiedKFold\n\nskf = StratifiedKFold(n_splits = 5, shuffle = True)\n\ncross_validate(tree_model, X, y, cv = skf)","execution_count":29,"outputs":[{"output_type":"execute_result","execution_count":29,"data":{"text/plain":"{'fit_time': array([0.02637315, 0.0282464 , 0.02617359, 0.02623463, 0.02175307]),\n 'score_time': array([0.00463295, 0.00499821, 0.00511384, 0.00459814, 0.00488615]),\n 'test_score': array([0.58441558, 0.67532468, 0.63636364, 0.61038961, 0.59210526])}"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"O *scikit-learn* também oferece suporte para realizar iterações no StratifiedKFold, como fizemos na função `roda_n_modelos`. Para isso, temos que usar o **ReapeatedStratifiedKFold**."},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import RepeatedStratifiedKFold\n\nrskf = RepeatedStratifiedKFold(n_splits = 5, n_repeats = 10)\n\ncross_validate(tree_model, X, y, cv = rskf)","execution_count":30,"outputs":[{"output_type":"execute_result","execution_count":30,"data":{"text/plain":"{'fit_time': array([0.02597523, 0.02318287, 0.02792144, 0.02568674, 0.02684355,\n        0.02654719, 0.02783394, 0.02791739, 0.02501845, 0.0247407 ,\n        0.0272553 , 0.02415776, 0.02855849, 0.02583981, 0.02302957,\n        0.02919626, 0.02547264, 0.02521849, 0.02353358, 0.02564573,\n        0.02494693, 0.0257833 , 0.02288246, 0.02748084, 0.02590036,\n        0.02827859, 0.02886057, 0.02645373, 0.02435017, 0.02706122,\n        0.0277648 , 0.02586842, 0.02261901, 0.02656174, 0.02486658,\n        0.02621198, 0.02528596, 0.02460408, 0.03163791, 0.03161812,\n        0.03038001, 0.03064418, 0.02990055, 0.02617335, 0.02441096,\n        0.02627063, 0.02310228, 0.02628589, 0.03730583, 0.0271666 ]),\n 'score_time': array([0.00477481, 0.00470161, 0.00486708, 0.00454187, 0.00456023,\n        0.00496078, 0.00511146, 0.00457931, 0.00443411, 0.00470114,\n        0.00468993, 0.00464511, 0.00453186, 0.00537515, 0.00463104,\n        0.00454354, 0.00449061, 0.00452709, 0.00452471, 0.00467658,\n        0.00450134, 0.00455642, 0.00520062, 0.00477099, 0.00466156,\n        0.00481987, 0.00643706, 0.00480247, 0.00456071, 0.0045681 ,\n        0.00455046, 0.00458241, 0.00456691, 0.00481033, 0.00461936,\n        0.00481725, 0.00489306, 0.00481677, 0.00469542, 0.00470042,\n        0.00487518, 0.00548244, 0.00455451, 0.00476336, 0.00500345,\n        0.00482893, 0.00467634, 0.00465155, 0.00488162, 0.00538254]),\n 'test_score': array([0.64935065, 0.64935065, 0.66233766, 0.67532468, 0.59210526,\n        0.64935065, 0.53246753, 0.58441558, 0.5974026 , 0.61842105,\n        0.7012987 , 0.68831169, 0.62337662, 0.57142857, 0.57894737,\n        0.66233766, 0.66233766, 0.66233766, 0.50649351, 0.64473684,\n        0.54545455, 0.63636364, 0.5974026 , 0.67532468, 0.69736842,\n        0.62337662, 0.64935065, 0.58441558, 0.57142857, 0.71052632,\n        0.58441558, 0.72727273, 0.64935065, 0.7012987 , 0.51315789,\n        0.66233766, 0.5974026 , 0.58441558, 0.62337662, 0.64473684,\n        0.63636364, 0.64935065, 0.57142857, 0.5974026 , 0.69736842,\n        0.58441558, 0.58441558, 0.74025974, 0.5974026 , 0.60526316])}"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Vamos agora implementar o Cross-Validate na função que criamos anteriormente:"},{"metadata":{"trusted":true},"cell_type":"code","source":"def roda_modelo_cv(modelo, dados, n_splits, n_repeat):\n    \n    np.random.seed(0)\n    \n    dados.sample(frac=1).reset_index(drop = True)\n    \n    X = dados.drop([\"ICU\", \"WINDOW\"], axis = 1)\n    y = dados['ICU']\n    \n    rskf = RepeatedStratifiedKFold(n_splits = n_splits, n_repeats = n_repeat)\n    resultados = cross_validate(modelo, X, y, cv = rskf, scoring = 'roc_auc')\n    \n    auc_medio = np.mean(resultados['test_score'])\n    auc_std = np.std(resultados['test_score'])\n    \n    print(f\"AUC Médio: {auc_medio}\")\n    print(f\"AUC STD: {auc_std}\")\n    print(f\"LOW AUC: {auc_medio - 2*auc_std} - HIGH AUC: {auc_medio + 2*auc_std}\")\n    \nroda_modelo_cv(lr_model, df_limpo, 5, 10)","execution_count":31,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n","name":"stderr"},{"output_type":"stream","text":"AUC Médio: 0.7885979501768975\nAUC STD: 0.05482282688640678\nLOW AUC: 0.6789522964040839 - HIGH AUC: 0.898243603949711\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Desafio 08: Testar outros splitters e observar as diferenças."},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import LeaveOneOut\n\ndef roda_modelo_loo(modelo, dados):\n    \n    np.random.seed(0)\n    \n    dados.sample(frac=1).reset_index(drop = True)\n    \n    X = dados.drop([\"ICU\", \"WINDOW\"], axis = 1)\n    y = dados['ICU']\n    \n    loo = LeaveOneOut()\n  \n    resultados = cross_validate(modelo, X, y, cv = loo)\n    \n    auc_medio = np.mean(resultados['test_score'])\n    auc_std = np.std(resultados['test_score'])\n    \n    print(f\"AUC Médio: {auc_medio}\")\n    print(f\"AUC STD: {auc_std}\")\n    print(f\"LOW AUC: {auc_medio - 2*auc_std} - HIGH AUC: {auc_medio + 2*auc_std}\")\n    \nroda_modelo_loo(tree_model, df_limpo)","execution_count":32,"outputs":[{"output_type":"stream","text":"AUC Médio: 0.65625\nAUC STD: 0.47495887979908324\nLOW AUC: -0.2936677595981665 - HIGH AUC: 1.6061677595981665\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"markdown","source":"## Aula 05 - Desafios em Machine Learning"},{"metadata":{},"cell_type":"markdown","source":"Na última aula, após a criação das funções que nos ajudam a rodar os modelos, deu pra perceber que a **árvore de decisão não obtever um resultado bom** quando comparada com o modelo usando Logistic Regression. Árvores de decisão, quando usadas sozinhas, geralmente não trazem grandes resultados. \n<br>\n\n![image.png](attachment:image.png)\n\nAinda assim, podemos ajustar os **hiperparâmetros** desse algoritmo e tentar alcançar resultados ainda melhores. Um desses parâmetros é o **max_depth**, que define a profundeza máxima que essa árvore pode chegar, fazendo com que a árvore tome mais decisões para se moldar ao set de treino.","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(5,36,10):\n    print(f\"Árvore com max_depth = {i}\")\n    tree_model_tunning = DecisionTreeClassifier(max_depth = i) \n    roda_modelo_cv(tree_model_tunning, df_limpo, 5, 10)\n    print(\"\")","execution_count":33,"outputs":[{"output_type":"stream","text":"Árvore com max_depth = 5\nAUC Médio: 0.6765995550206079\nAUC STD: 0.05865294710818543\nLOW AUC: 0.559293660804237 - HIGH AUC: 0.7939054492369787\n\nÁrvore com max_depth = 15\nAUC Médio: 0.6503939161833898\nAUC STD: 0.05266694452085566\nLOW AUC: 0.5450600271416786 - HIGH AUC: 0.7557278052251011\n\nÁrvore com max_depth = 25\nAUC Médio: 0.6500430389904074\nAUC STD: 0.05320883016273904\nLOW AUC: 0.5436253786649293 - HIGH AUC: 0.7564606993158854\n\nÁrvore com max_depth = 35\nAUC Médio: 0.6500430389904074\nAUC STD: 0.05320883016273904\nLOW AUC: 0.5436253786649293 - HIGH AUC: 0.7564606993158854\n\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"Para entender melhor os motivos no qual o aumento na profundeza máxima da árvore não retorna resultados melhores, vamos adiconar à função `roda_modelo_cv` os scores do modelo no set de treino (ao invés de apenas no score de teste). "},{"metadata":{"trusted":true},"cell_type":"code","source":"def roda_modelo_cv_train(modelo, dados, n_splits, n_repeat):\n    \n    np.random.seed(0)\n    \n    dados.sample(frac=1).reset_index(drop = True)\n    \n    X = dados.drop([\"ICU\", \"WINDOW\"], axis = 1)\n    y = dados['ICU']\n    \n    rskf = RepeatedStratifiedKFold(n_splits = n_splits, n_repeats = n_repeat)\n    resultados = cross_validate(modelo, X, y, cv = rskf, scoring = 'roc_auc', return_train_score = True)\n    \n    auc_medio = np.mean(resultados['test_score'])\n    auc_std = np.std(resultados['test_score'])\n    \n    auc_medio_treino = np.mean(resultados['train_score'])\n    auc_std_treino = np.std(resultados['train_score'])\n    \n    print(\"TEST SET\\n\")\n    print(f\"AUC Médio: {auc_medio}\")\n    print(f\"AUC STD: {auc_std}\")\n    print(f\"LOW AUC: {auc_medio - 2*auc_std} - HIGH AUC: {auc_medio + 2*auc_std}\")\n    \n    print(\"\\nTRAINING SET\\n\")\n    print(f\"AUC Médio: {auc_medio_treino}\")\n    print(f\"AUC STD: {auc_std_treino}\")\n    print(f\"LOW AUC: {auc_medio_treino - 2*auc_std_treino} - HIGH AUC: {auc_medio_treino + 2*auc_std_treino}\")","execution_count":37,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(5,36,10):\n    print(f\"Árvore com max_depth = {i}\")\n    tree_model_tunning = DecisionTreeClassifier(max_depth = i) \n    roda_modelo_cv_train(tree_model_tunning, df_limpo, 5, 10)\n    print(\"\")","execution_count":38,"outputs":[{"output_type":"stream","text":"Árvore com max_depth = 5\nTEST SET\n\nAUC Médio: 0.6765995550206079\nAUC STD: 0.05865294710818543\nLOW AUC: 0.559293660804237 - HIGH AUC: 0.7939054492369787\n\nTRAINING SET\n\nAUC Médio: 0.9325161094279254\nAUC STD: 0.012965703228334131\nLOW AUC: 0.9065847029712572 - HIGH AUC: 0.9584475158845936\n\nÁrvore com max_depth = 15\nTEST SET\n\nAUC Médio: 0.6503939161833898\nAUC STD: 0.05266694452085566\nLOW AUC: 0.5450600271416786 - HIGH AUC: 0.7557278052251011\n\nTRAINING SET\n\nAUC Médio: 0.9999966038376634\nAUC STD: 1.6637729616459098e-05\nLOW AUC: 0.9999633283784305 - HIGH AUC: 1.0000298792968962\n\nÁrvore com max_depth = 25\nTEST SET\n\nAUC Médio: 0.6500430389904074\nAUC STD: 0.05320883016273904\nLOW AUC: 0.5436253786649293 - HIGH AUC: 0.7564606993158854\n\nTRAINING SET\n\nAUC Médio: 1.0\nAUC STD: 0.0\nLOW AUC: 1.0 - HIGH AUC: 1.0\n\nÁrvore com max_depth = 35\nTEST SET\n\nAUC Médio: 0.6500430389904074\nAUC STD: 0.05320883016273904\nLOW AUC: 0.5436253786649293 - HIGH AUC: 0.7564606993158854\n\nTRAINING SET\n\nAUC Médio: 1.0\nAUC STD: 0.0\nLOW AUC: 1.0 - HIGH AUC: 1.0\n\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"Aqui podemos ver com clareza que enquanto os scores de teste se mantém ou até diminuem, os resultados do set de treino chegam em 100%. Isso acontece pois a árvore se moldou tão bem para o set de treino que a generalização para os dados de teste não acontece.\n<br>\n\nEsse problema é chamado de **overfitting**. Para resolvê-lo, temos que utilizar hiperparâmetros de regularização ou, no caso de modelos baseados em decision trees, de **poda**."},{"metadata":{},"cell_type":"markdown","source":"#### Desafio 09 - Criar um gráfico dos valores médios de ROC AUC de treino e de teste. "},{"metadata":{"trusted":true},"cell_type":"code","source":"def roda_modelo_cv_train(modelo, dados, n_splits, n_repeat, retorna_valor = False):\n    \n    np.random.seed(0)\n    \n    dados.sample(frac=1).reset_index(drop = True)\n    \n    X = dados.drop([\"ICU\", \"WINDOW\"], axis = 1)\n    y = dados['ICU']\n    \n    rskf = RepeatedStratifiedKFold(n_splits = n_splits, n_repeats = n_repeat)\n    resultados = cross_validate(modelo, X, y, cv = rskf, scoring = 'roc_auc', return_train_score = True)\n    \n    auc_medio = np.mean(resultados['test_score'])\n    auc_std = np.std(resultados['test_score'])\n    \n    auc_medio_treino = np.mean(resultados['train_score'])\n    auc_std_treino = np.std(resultados['train_score'])\n    \n    if retorna_valor:\n        return auc_medio, auc_medio_treino\n    \n    else:\n        print(\"TEST SET\\n\")\n        print(f\"AUC Médio: {auc_medio}\")\n        print(f\"AUC STD: {auc_std}\")\n        print(f\"LOW AUC: {auc_medio - 2*auc_std} - HIGH AUC: {auc_medio + 2*auc_std}\")\n\n        print(\"\\nTRAINING SET\\n\")\n        print(f\"AUC Médio: {auc_medio_treino}\")\n        print(f\"AUC STD: {auc_std_treino}\")\n        print(f\"LOW AUC: {auc_medio_treino - 2*auc_std_treino} - HIGH AUC: {auc_medio_treino + 2*auc_std_treino}\")","execution_count":40,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"auc_test_list = []\nauc_treino_list = []\n\nfor i in range(1,20):\n    tree_model_tunning = DecisionTreeClassifier(max_depth = i) \n    auc_test, auc_treino = roda_modelo_cv_train(tree_model_tunning, df_limpo, 5, 10, retorna_valor = True)\n    auc_test_list.append(auc_test)\n    auc_treino_list.append(auc_treino)","execution_count":66,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import seaborn as sns\nsns.set_palette(\"colorblind\")\n\ndef plot_train_test_scores(train, test, parameter_range, parameter_name = 'parameter'):\n    fig, ax = plt.subplots(figsize = (8,4))\n    x = parameter_range\n\n    sns.lineplot(x = x, y = test, ax = ax, label = \"Test Set\")\n    sns.lineplot(x = x, y = train, ax = ax, label = \"Train Set\")\n\n    ax.set(xlabel=parameter_name, ylabel='Score ROC AUC',\n           title='')\n\n    ax.set_xticks(x)\n\n    plt.grid()\n\n    ax;","execution_count":64,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plot_train_test_scores(auc_treino_list, auc_test_list, np.arange(1,20), 'max_depth')","execution_count":67,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"#### Desafio 10: Criar um overfitting com outra base de dados, outro modelo de ML e plotar o gráfico para análise.\n\n#### Desafio 11: Estudar um pouco mais sobre overfit e underfit\n\nO uso de uma segunda base de dados fica fora do escopo desse notebook. Utilizarei o Logistic Regression nessa mesma base de dados para resolver o desafio aqui mesmo.\n<br>\n\nO Logistic Regression possui muitos hiperparâmetros. No entanto, o maior destaque entre eles é o parâmetro de regularização, chamado de **C**. Quanto menor o valor de C, maior a regularização. \n<br>\nA regularização é uma forma de **controlar o overfitting**, fazendo com que o modelo fique menos complexo e mais propenso a generalização. No entanto, um valor muito pequeno (ou seja, uma regularização alta demais) causa outro fenômeno, chamado de **underfitting**.\n\nO underfitting é o oposto do overfitting. Enquanto o overfit é um fenômeno em que o modelo se molda perfeitamente ao dataset e não responde bem a novos dados, o underfit é quando o modelo fica simples demais e perde as nuances dos dados. "},{"metadata":{"trusted":true},"cell_type":"code","source":"import warnings\n\ndef fxn():\n    warnings.warn(\"deprecated\", DeprecationWarning)\n\nwith warnings.catch_warnings():\n    warnings.simplefilter(\"ignore\")\n    fxn()\n    auc_test_list = []\n    auc_treino_list = []\n\n    for i in np.arange(0.1,10,0.5):\n        lr_model_tunning = LogisticRegression(max_iter = 2000, C = i) \n        auc_test, auc_treino = roda_modelo_cv_train(lr_model_tunning, df_limpo, 5, 10, retorna_valor = True)\n        auc_test_list.append(auc_test)\n        auc_treino_list.append(auc_treino)\n        \nplot_train_test_scores(auc_treino_list, auc_test_list, np.arange(0.1,10,0.5), 'C')","execution_count":61,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n","name":"stderr"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/opt/conda/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n","name":"stderr"},{"output_type":"display_data","data":{"text/plain":"<Figure size 576x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"Para finalizar a aula, vamos utilizar um modelo chamado de **Random Forest**. Ele é basicamente um conjunto de árvores de decisão, com cada árvore utilizando um número máximo `max_features` de features e apenas uma sample (retirada utilizando o método de `bootstrap`) do dataset. \n<br>\nPor causa da forma que ele funciona, o algoritmo é muito menos propenso ao overfit. Apesar de poder acabar alcançando scores > .95 em subsets de treino, isso não torna ele fraco para o set de teste.   "},{"metadata":{"trusted":true},"cell_type":"code","source":"auc_test_list = []\nauc_treino_list = []\n\nfor i in range(1,20):\n    rf_model = RandomForestClassifier(max_depth = i) \n    auc_test, auc_treino = roda_modelo_cv_train(rf_model, df_limpo, 5, 10, retorna_valor = True)\n    auc_test_list.append(auc_test)\n    auc_treino_list.append(auc_treino)\n    \nplot_train_test_scores(auc_treino_list, auc_test_list, np.arange(1,20), 'max_depth')","execution_count":71,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAf4AAAEHCAYAAABP8iT0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAABCR0lEQVR4nO3deXxU9dX48c+ZyU5CEraIBgQFWaSAxoJK1cStaFW0D1ar5XEttZW6W5dutj5tqdr6q9antI+l7kVbN6qoWAjizmJRWWUVA8gSEpKQfeb8/rg3YQjZCPfOJJnzfr3mNfd+7/eeORNIzr3fu4mqYowxxpj4EIh1AsYYY4yJHiv8xhhjTByxwm+MMcbEESv8xhhjTByxwm+MMcbEkYRYJxANffr00UGDBnkWb+/evfTo0cOzeNGIbTl3/diWc3RiW87Rid0Vc/Yzttdxly5duktV+za7UFW7/SsvL0+9VFhY6Gm8aMS2nLt+bMs5OrEt5+jE7oo5+xnb67jAEm2hJtpQvzHGGBNHrPAbY4wxccQKvzHGGBNH4uLkvubU1dVRVFREdXX1Qa+bmZnJqlWrfMjKv9hexU1JSSE3N5fExEQPsjLGGBNtcVv4i4qKyMjIYNCgQYjIQa1bXl5ORkaGL3n5FduLuKpKcXExRUVFDB482KPMjDHGRJNvQ/0iMlNEdojI8haWi4g8JCLrROQTETk+YtlEEVnjLrszor2XiLwpImvd9+yO5lddXU3v3r0PuujHMxGhd+/eHRolMcYY0zn4eYz/MWBiK8vPAYa6r6nAnwBEJAg84i4fCXxbREa669wJzFPVocA8d77DrOgfPPuZGWNM1+bbUL+qLhSRQa10mQQ84V5v+IGIZIlIf2AQsE5VNwCIyCy370r3Pd9d/3FgAXCHH/kbE0uqitZXorUVhOucV9LeVVRvTQHCoGFUnXc0FDEduSzUZD6yr+4XJ33Xaso+Wds0i+YSaynjZlvTd31G2cerD+En0TK/YlvO0YndFXP2M3ZayXb2lTd/ibb4i+xBcKfwv6Kqo5pZ9gowXVXfcefn4RTxQcBEVb3WbZ8CjFfVaSJSqqpZETFKVLXZ4X4RmYozkkBOTk7erFmz9luemZnJkCFDOvS9QqEQwWCwQ+s2KC4u5oILLgBg+/btBINB+vTpg6qyYMECkpKSWl3/7bffJikpifHjxx+wbMeOHVx//fVs2bKFuro6Bg4cyHPPPddizqWlpfzjH//gu9/9brtyX7duHXv27Gmcr6ioID09vV3rHgy/4voWO1xLQu0Oqit20SNZCISrkHAVgXA1Eqpy56uRcLUzHdHWMN84Ha5GWiimxpjupyppMDtG/tmzeAUFBUtV9YTmlsXy5L7mxoy1lfaDoqp/Af4CcMIJJ2h+fv5+y1etWtXhk928OFEuIyODTz75BIB77rmH9PR0brvttnbHXrRoEenp6Zx55pkHLLvttts455xzuPHGGwH45JNPCAaDLcYtLi5m5syZ3HLLLe3KPSUlheOOO65xfsGCBTT9+XrBr7iHElvD9dSXfU5dyVrqStdRV7qW+hL3vexz2vyvGkwikJiOJKYTSOxBICUdSepLILGH25aOJKU3me9BIDGdT1d+xujRYxEJQMTLmQ9GTLsvBAkEI+YjlwcjpoX33/+Ak04+uZmED/x1lGZ/RYFmDgO9++67TJgwoa0fa4f4Fdtyjk7srpizn7Hffe8D3/7eNRXLwl8EDIiYzwW2AkkttANsF5H+qrrNPSywIyqZRsnSpUu58cYbqaqqok+fPjz22GP079+fhx56iBkzZpCQkMDIkSOZPn06M2bMIBgM8tRTT/Hwww9zyimnNMbZtm0bZ599duP86NGjKS8vB+D+++/nueeeo6amhosuuohf/OIX3Hnnnaxfv56xY8dy1llncf/990f9u3cmqkqoYotT2EvWOkW9dK0zvWcjhOsa+0pSTxKzhpDcfzzpIy4nIfMoVq4tYvTxJ+1XtBsKvQRbH8lpTXXRAtIG5XvwDQ8USuxNQo/+nscNJ2YTTOvneVw/Y1vO0YndFXP2M3Y4oafnMVsSy8I/G5jmHsMfD+xxC/pOYKiIDAa2AJcCl0WscwUw3X1/2YtEbnp5OR9vKWt3/1ConmCw9R/dmCN68v8mHXCEo0Wqyg9/+EOefvppBg8ezLPPPsuPf/xjZs6cyfTp09m4cSPJycmUlpaSlZXFdddd1zhK0NT111/PJZdcwh//+EfOPPNMrrrqKjIyMpg7dy5r165l0aJFqCoXXHABCxcuZPr06Sxfvpxly5a1O98uT5VQ5c79iruz976WutL1aH1lY1cJppCQdTSJvUeSdvQkErOHkpg1lMSsIQTS+h1wwmPVjgWkDsiP8hcyxpj28a3wi8jfcc5U6CMiRcDPgUQAVZ0BzAHOBdYBlcBV7rJ6EZkGvAEEgZmqusINOx14TkSuATYDF/uVf7TV1NSwfPlyJk2aRCAQIBQK0b+/swc2evRoLr/8ci688EIuvPDCNmN9/etfZ8OGDbz++uu89tprHHfccXzwwQfMnTuXuXPnNg7TV1RUsHbtWgYOHOjnV+s0wnWVVK5/mYpVz5Bb9C6bP67YtzCQQELPQSRmH0PKgAKnsGc7xT2YkesMixtjTDfg51n9325juQLXt7BsDs6GQdP2YuAMTxKMcDB75uDPTXZUlWOPPZa5c+ceEPvVV19l4cKFzJ49m3vvvZcVK1a0EGWfXr16cdlll3HZZZdx3nnn8e6776Kq3HXXXXzve9/br++mTZu8/CqdiqpSveUdKlY9yd61z6O15SRkHEllVj5HHptPYvYQErOGktBzEBK0uxEaY7o/243pJJKTk9m5cycffvgh4NxSeMWKFYTDYb744gsKCgq47777KC0tpaKigoyMjMbj9k3Nnz+fykpnqLq8vJz169eTm5vL17/+dWbOnElFhbOnu2XLFnbs2NFqrK6qbs8GSt7/JUV/G86X/zyDvZ/9kx5DLuKw/3qT3KvXsHvADWQefwNpg88lMXuoFX1jTNyI21v2djaBQIB//vOfXH/99dx6663U19dz0003ccwxx/Cd73yHPXv2oKrcfPPNZGVlcf755zN58mRefvnlA07uW7p0KdOmTSMhIYFwOMy1115LXl4eGRkZrFq1ipNOOgmA9PR0nnrqKY4++mgmTJjAqFGjOOecc7rsyX3hmjL2rn2e8lVPUrPlHUBIGVBA1kk/pceQiwgk9oh1isYYE3NW+DuBe+65p3H69ddfP2Co/5133jlgnWOOOabxcsCmbr/9dm6//fb92hr26G+88cbGy/wiPfPMMwebdqeg4RBVX8ynYuWTVK5/Ga2vIjF7KNkn/9I5yz5jQNtBjDEmjljhN11S7e5VVKx8korVfydUsYVAcjbpI/+b9BFTSD7sq3ZrYWOMaYEVftNlhKqK2fvZc5SvfJLa7UtAgqQO+joZpz1A2uDzkITkWKdojDGdnhV+06lpqI7KTa9TsepJKje8CuE6kvqMptep95M+7FKCPXJinaIxxnQpVvhNpxSq3En2lj+x+dHLCVftJJDWj55jfkD6yO+Q3HdMrNMzxpguywq/6XRqvlzM9lcvJaNiGylDLiBjxBRSjzzbLrkzxhgPWOE3nYaqUv7poxS/dTMJPQ5n29CHmHDutbFOyxhjuhW7gU+MFBcXM3bsWMaOHcthhx3GEUccwdixY5kwYQK1tbWtrrtkyRJuuOGGg/q8J598kq985SuMHj2aUaNG8fLLrT/m4KWXXmLlypUH9RmHIlxfxa43v0vx/OtJzS3g8Ms+oC6tY49NNsYY0zLb44+R3r17Nz4Up+ljeZOSkqivrychofl/nhNOOIETTmj2McvNKioq4v7772fZsmVkZmZSUVHBzp07W13npZde4rzzzmPkyJHt/pyOqtuzkR2vXELtzmVkjf8JWSf+xO6Nb4wxPrG/rp3IlVdeyV133UVBQQF33HEHixYt4uSTT+a4447j5JNPZs2aNYDzLPnzzjsPcDYarr76avLz8znqqKN46KGHDojbcFve9PR0wLlj3+DBgwFYv349EydOJC8vj1NOOYXVq1fz3nvvMXv2bG6//XbGjh3L+vXrffvOlRtfZ+szJ1JftomcSS+RfdLPrOgbY4yPbI8fKF5wK7U7P253//pQiIpgsNU+SX3H0Dv/dwedy7p16/j3v/9NMBikrKyMhQsXkpCQwL///W/uvvtunn/++QPWWb16NYWFhZSXlzNs2DC+//3vk5i470S4MWPG0LdvXwYPHswZZ5zBN7/5Tc4//3wApk6dyowZMxg6dCgffvghP/jBD5g/fz4XXHAB5513HpMnTz7o79AeqmFKP/wVpR/8D0l9vkK/858jMfMoXz7LGGPMPlb4O5kLL7yQoLtRsWfPHq644grWrl2LiFBXV9fsOt/4xjdITk4mOTmZfv36sX37dnJzcxuXB4NBXnzxRVavXs28efO4+eabWbp0KbfddhvvvfceF1+87+nGNTU1/n5BIFRdws7Xr6Rq02ukj/gOvU//I4HENN8/1xhjjBV+gIPeM/fjsbwNevTY9yCZn/70pxQUFPDiiy+yadMm8vPzm10nOXnfHeuCwSD19fUH9BERxo0bx7hx4zjrrLO46qqruOWWW8jKymo81yAaanb8hx2vXEp9RRG9Cx4mY/RUu72uMcZEkR1M7cT27NnDEUccAcBjjz3W4Thbt27dr7gvW7aMI488kp49ezJ48GD+8Y9/AM7ldB9/7Bzy8ONRveUrn2Dbs6eh4Vr6XzyfnmO+Z0XfGGOizNfCLyITRWSNiKwTkTubWZ4tIi+KyCciskhERrntw0RkWcSrTERucpfdIyJbIpad6+d3iKUf/ehH3HXXXUyYMIFQKNThOHV1dfzkJz9h+PDhjB07lmeffZY//OEPADz99NP89a9/ZcyYMRx77LGNl/ldeuml3H///Rx33HGHfHKf1tewa/4P2TX3WpL7n8gRl31ISv/xhxTTGGNMx/g21C8iQeAR4CygCFgsIrNVNfLi8LuBZap6kYgMd/ufoaprgLERcbYAL0as96CqPuBX7tEW+VjeyL3sk046ic8++6xx/t577wUgPz+/cdg/cl2A5cuXHxD/yCOP5JVXXmn28MTgwYN5/fXXD2ifMGGCJ9fx15cXsePVS6n5chGZebeSPeFeJGBHmIwxJlb83OMfB6xT1Q2qWgvMAiY16TMSmAegqquBQSLS9KkrZwDrVfVzH3M1Pqj6opAtz4yntngl/b4xi16n/MaKvjHGxJioqj+BRSYDE1X1Wnd+CjBeVadF9Pk1kKKqt4jIOOA9t8/SiD4zgY9U9Y/u/D3AlUAZsAS4VVVLmvn8qcBUgJycnLxZs2bttzwzM5MhQzp2Z7hQKNR45r3X/IrtZdx169axZ8+exvmKiorGewQAoErPHf8ga9tM6pJz2Tn4Z9SnDDzozzkgroe6YmzLOTqxLefoxO6KOfsZ2+u4BQUFS1W1+Tu9qaovL+Bi4NGI+SnAw0369AT+BiwDngQWA2MilicBu4CciLYcIIgzWvErYGZbueTl5WlTK1eu1HA4fEB7e5SVlXVovVjG9ipuOBzWlStX7tdWWFjYOB2q3qNfzp6sGx5M1O2vXKqhmvIOf1ZkXK91xdiWc3RiW87Rid0Vc/YzttdxgSXaQk30c9y1CBgQMZ8LbI3soKplwFUA4pzevdF9NTgHZ29/e8Q6jdMi8n/AKx1JLiUlheLiYnr37m1nlreTqlJcXExKSkqzy2uLV7DjlW9RV7qBXqfeR8/jbrSfrTHGdDJ+Fv7FwFARGYxzct6lwGWRHUQkC6hU5xyAa4GF7sZAg28Df2+yTn9V3ebOXgQceDZbO+Tm5lJUVNTmPeubU11d3WLxO1R+xfYqbkpKyn43B2pQseY5dv37ewQS0znsv+aSmnvKIX+WMcYY7/lW+FW1XkSmAW/gDM3PVNUVInKdu3wGMAJ4QkRCwErgmob1RSQN54qA7zUJfZ+IjAUU2NTM8nZJTExsvF/9wVqwYAHHHXdch9aNVWy/4mqojuwtM9i57AWS+59Ev2/8nYT0wz3/HGOMMd7w9RRrVZ0DzGnSNiNi+n1gaAvrVgK9m2mf4nGapoM0XM/2ly+k58436Tn2enqd8lskmBTrtIwxxrTC7txnOqzk/Xuo2vwmxbk30Dv/QSv6xhjTBVjhNx1SuekN9iy+j/RRV1PR57xYp2OMMaadrPCbg1ZfXsTON64isc8oeuc/GOt0jDHGHAQr/OagaLieHa9NQeur6Hfu3wkkpMY6JWOMMQfB7p9qDkrJez+nZuu79J34GEm9hsU6HWOMMQfJ9vhNu1VufJ09S+4nY9Q1pA+/rO0VjDHGdDpW+E27NBzXT+rzFXrl/z7W6RhjjOkgK/ymTRqqY8dr30FDNfQ99xk7rm+MMV2YHeM3bSp5/+fUbH2PvhOfsOP6xhjTxdkev2lV5cbX2LPkATJGXUv68EtjnY4xxphDZIXftKi+/At2vnE1SX1G0yv/d7FOxxhjjAes8JtmaaiOHXOc4/r9vmHH9Y0xpruwY/ymWSXv/Yyabe/Td+ITJGYfE+t0jDHGeMT2+M0BKjfOYc/S35Hxle/acX1jjOlmrPCb/ex3XP80O65vjDHdjRV+08g5rn85Gqql3zf+TiAhJdYpGWOM8ZivhV9EJorIGhFZJyJ3NrM8W0ReFJFPRGSRiIyKWLZJRD4VkWUisiSivZeIvCkia933bD+/Qzwpee+n1Gz7gD5n/onE7KGxTscYY4wPfCv8IhIEHgHOAUYC3xaRkU263Q0sU9XRwH8Df2iyvEBVx6rqCRFtdwLzVHUoMM+dN4eocsOr7Fn6ezK+MpX0YZfEOh1jjDE+8XOPfxywTlU3qGotMAuY1KTPSJzijaquBgaJSE4bcScBj7vTjwMXepZxnKov28zOudeQ1HcMvU57INbpGGOM8ZGoqj+BRSYDE1X1Wnd+CjBeVadF9Pk1kKKqt4jIOOA9t89SEdkIlAAK/FlV/+KuU6qqWRExSlT1gOF+EZkKTAXIycnJmzVrlmffraKigvT0dM/iRSN2i3G1npy1t5FUvYltwx6hPvkI72Ifoq74c/YztuUcndiWc3Rid8Wc/YztddyCgoKlTUbL91FVX17AxcCjEfNTgIeb9OkJ/A1YBjwJLAbGuMsOd9/7AR8Dp7rzpU1ilLSVS15ennqpsLDQ03jRiN1S3OKFd+iGBxO1fPWznsc+VF3x5+xnbMs5OrEt5+jE7oo5+xnb67jAEm2hJvp5A58iYEDEfC6wNbKDqpYBVwGIiAAb3RequtV93yEiL+IcOlgIbBeR/qq6TUT6Azt8/A7dWuWGV5zj+qO/R/qwb8U6HWOMMVHg5zH+xcBQERksIknApcDsyA4ikuUuA7gWWKiqZSLSQ0Qy3D49gLOB5W6/2cAV7vQVwMs+foduq75sMzvfuIakvmPpder9sU7HGGNMlPi2x6+q9SIyDXgDCAIzVXWFiFznLp8BjACeEJEQsBK4xl09B3jRGQQgAXhGVV93l00HnhORa4DNOIcUzEHQUK1zvb7Wu/fht+v1jTEmXvh6r35VnQPMadI2I2L6feCAC8ZVdQMwpoWYxcAZ3mYaX3a/+1NqvvyQvuc+TWLWkFinY4wxJorszn1xZu/6f1H20YNkjL6O9GNssMQYY+KNFf44Ulf2ObvmXktSv+Podep9sU7HGGNMDFjhjxfhOnbOuRzVEP3OteP6xhgTr3w9xm86j+xtM6nZuYh+5z5DYtbRsU7HGGNMjNgefxyo3Pg6PXc+T8aY79PjmMmxTscYY0wMWeHv5sJ1lRQX3kBtypH0PsWO6xtjTLxrsfCLSIqI9G2mvZ+I2AHiLqJ08XTqyzaxO/eHSEJyrNMxxhgTY63t8T8EnNJM+1nAg/6kY7xUV/IZe5b+nh7DL6MmfXSs0zHGGNMJtFb4v6aqLzRtVNWngVP9S8l4QVXZVXgjEkyh1ynTY52OMcaYTqK1wi8dXM90AnvX/pPqzfPodfIvSehxWKzTMcYY00m0VsB3iMi4po0i8lVgp38pmUMVri1n91u3k9R3LBmjvxfrdIwxxnQirV3HfzvOw3AeA5a6bScA/43zpD3TSZV88D+E9m6l33nPIoFgrNMxxhjTibS4x6+qi4DxOEP+V7ovAcar6ofRSM4cvNpdn1L2n4fIGHUNKf3HxzodY4wxnUyrd+5T1e3Az6OUizlEDSf0BZKzyJ7wP7FOxxhjTCfUYuEXkU8BjWhSYBdQCDygqtU+52YOUsWqp6jZ8g59zpxBMLV3rNMxxhjTCbW2x39eM229gCuAh4Hv+pKR6ZBQdQm7376T5MPGk37slbFOxxhjTCfV2jH+z5t5/UdVb8I5ya9NIjJRRNaIyDoRubOZ5dki8qKIfCIii0RklNs+QEQKRWSViKwQkRsj1rlHRLaIyDL3de7Bf+3up+S9nxOuLqb36Q8jYldbGmOMaV5HK0Sb64lIEHgEOAcYCXxbREY26XY3sExVR+NcLfAHt70euFVVRwAnAtc3WfdBVR3rvuZ08Dt0GzXbP6L8kz/Tc8wPSO43NtbpGGOM6cRaO8Z/fDPN2cB3gIXtiD0OWKeqG9x4s4BJwMqIPiOB3wCo6moRGSQiOaq6DdjmtpeLyCrgiCbrGkDDIXbNn0YwLYfsk+w8TGOMMa0TVW1+gUhhkyYFioEFwF9Uta7VwCKTgYmqeq07PwXnUsBpEX1+DaSo6i3uzYLec/ssjegzCGdDY5SqlonIPTiXFpYBS3BGBkqa+fypwFSAnJycvFmzZrWW7kGpqKggPT3ds3iHEjt91yv0LnqIXQPvYG+vMzyLezD8it0Vc/YztuUcndiWc3Rid8Wc/YztddyCgoKlqtr8YXlVPegXkNOOPhcDj0bMTwEebtKnJ/A3YBnwJLAYGBOxPB3n5kHfjPxsIIhzuOFXwMy2csnLy1MvFRYWehqvo7Hr9+7QTf/bT7f+40wNh8OexT1YfsXuijn7Gdtyjk5syzk6sbtizn7G9jousERbqImtXscfSUQygf8CLgNG4Ay9t6YIGBAxnwtsbbLRUQZc5cYXYKP7QkQSgeeBpzXiYUHq3FugIaf/A15p73fobna/czfhunJ6F/wB58dnjDHGtK7Vwi8iqcAFOMX+eCADuJD2HeNfDAwVkcHAFpzb/F7WJH4WUKmqtcC1wEJ1hvMF+CuwSlV/32Sd/uqcAwBwEbC8Hbl0O9Vb36Ni5eNk5t1KUu+m50waY4wxzWvt5L6Gx+/OBf4IzMc5WW9BewKrar2ITAPewBman6mqK0TkOnf5DJyRgydEJIRz4t417uoTcA4NfCoiy9y2u9U5g/8+ERmLc87BJiDunkKj4XqK5/+QYMYAssb/ONbpGGOM6UJa2+MfBZQAq4DVqhoSkebPBGyBW6jnNGmbETH9PjC0mfXeoYXHAqvqlIPJoTsqW/a/1O76lH7nPUsgyZ8TWIwxxnRPrd3AZwzwLZwT8P4tIm8DGSJiD3ePofqKrZR88AtSj/w6aUdfGOt0jDHGdDGt3ohHVVer6s9UdRhwM/AEsEhE3otKduYAuxf+CEK19C540E7oM8YYc9DafVa/qi4BlojIbTjH/k2UVW2ez97PniNr/E9IzBoS63SMMcZ0Qe0u/A3c6wPf8iEX0woN1VJceAMJmUeR+dXbY52OMcaYLuqgC7+JjT1LH6Su5DNyJs0mkJAa63SMMcZ0UfYYty6gruxzShf9mrSjJ5E2eGKs0zHGGNOFtVj4ReQWEbmmmfYfishNvmZl9rN7wS2A0Pu038U6FWOMMV1ca3v8V+PcP7+pv7jLTBRUbpxD5YZ/kTX+xyT0HBjrdIwxxnRxrRV+dW+l27SxhhZurmO8Fa6vorjwZhJ7DSfz+BtjnY4xxphuoNVj/CKS05424489i39LfdlGehc8hASTYp2OMcaYbqC1wn8/8KqInCYiGe4rH/gX8EA0kotndSVrKV3yAD2GXUrqgPxYp2OMMaabaPFyPlV9QkR2Ar/EuW8/OE/C+7mqvhaN5OKVqlK84GYkmEKvU38b63SMMcZ0I61ex+8WeCvyUVa57gWqPp9Lr9N+T0KP/rFOxxhjTDfS1jH+c0TkLRHZJSI73elzo5VcPJJQFcVv3UZS3zH0HHNdrNMxxhjTzbS4xy8i38V51v2PgCVu8wnAdBHJVdW/RCG/uJO5/SlCFVvod+7fkYDdWNEYY4y3WqssNwNfU9XdEW3zReQc4B2c6/mNh2qLV9BzxwukH3sVKYefGOt0jDHGdEOtDfVLk6IPgKoWtze4iEwUkTUisk5E7mxmebaIvCgin4jIIhEZ1da6ItJLRN4UkbXue3Z78+nMVJXi+TcSDqbR62u/inU6xhhjuqnWCn+ZiIxp2ui2lbcVWESCwCPAOcBI4NsiMrJJt7uBZao6Gvhv4A/tWPdOYJ6qDgXmufNdXsWqp6jespDS/lcTTO0T63SMMcZ0U60N9d8KzBaRvwFLAQW+ClwBfKcdsccB61R1A4CIzAImASsj+owEfgOgqqtFZJB7g6CjWll3EpDvrv84sAC4ox35dFqh6hJ2v30nyYeNp6L3ObFOxxhjTDcmqtryQpHDgB8Ax+LcpncF8IiqftlmYJHJwERVvdadnwKMV9VpEX1+DaSo6i0iMg54DxgPDG5pXREpVdWsiBglqnrAcL+ITAWmAuTk5OTNmjWrrZTbraKigvT0dM/i9friIdKL57DtmEcoCed4GruB1zlHI3ZXzNnP2JZzdGJbztGJ3RVz9jO213ELCgqWquoJzS5U1YN+ARPa0edi4NGI+SnAw0369AT+BizDeSDQYmBMa+sCpU1ilLSVS15ennqpsLDQs1jV2xbphgeTdNeCWz2PHcmvuH7G7oo5+xnbco5ObMs5OrG7Ys5+xvY6LrBEW6iJrV3OFwS+BRwBvKaqK0TkPJzj8qnAcW1scBQBAyLmc4GtTTY6yoCr3M8TYKP7Smtl3e0i0l9Vt4lIf2BHG3l0WhquZ9e86wn26E/2iT+LdTrGGGPiQGsn9/0VuBboDTzsHut/ALhPVdsq+uDsvQ8VkcEikgRcCsyO7CAiWe4y3M9a6G4MtLbubJzzDHDfX25HLp1S2Sd/pnbnMnqddj+B5J6xTscYY0wcaO3kvhOA0aoaFpEUYBcwRNtxfB9AVetFZBrwBhAEZrqjBte5y2cAI4AnRCSEc+LeNa2t64aeDjwnItcAm3EOC3Q59Xu3UfLez0kdeBY9hk6OdTrGGGPiRGuFv1ZVwwCqWi0in7W36DdQ1TnAnCZtMyKm3weGtnddt70YOONg8uiMdr91O4Rq6H36H3COchhjjDH+a63wDxeRT9xpAY525wVQda69Nx1Q9fm/2fvZc2Sd+FMSs4bEOh1jjDFxpLXCPyJqWcQRra9hV+ENJGQNIfOE22OdjjHGmDjTYuFX1c+jmUi8KF36APWl68i56FUCCSmxTscYY0ycafWxvMZbdaXr2bNoOj2GTibtyLNinY4xxpg4ZIU/SlSV4sKbIJhEr9MeiHU6xhhj4lS7Cr+IpIrIML+T6c4q171A1edvkH3iz0lIPzzW6RhjjIlTbRZ+ETkf55a6r7vzY0Vkdqsrmf2Ea8spfus2kvqOoefYH8Q6HWOMMXGsPXv89+A8aa8UQFWXAYP8Sqg7KvngXkIVW+l9+h+RQGsXUhhjjDH+ak/hr1fVPb5n0k3V7vyEsv88TMaoa0jpPz7W6RhjjIlz7dn9XC4ilwFBERkK3IDz+FzTBtUwu+ZPI5CSTfbX/ifW6RhjjDHt2uP/IXAsUAM8A+wBbvIxp26jYsXj1Gz7gF5fm04wpVes0zHGGGNa3+N3H807W1XPBH4cnZS6h1DVLna/cxfJR3yN9JFTYp2OMcYYA7Sxx6+qIaBSRDKjlE+3sfuduwnXltHn9IftITzGGGM6jfYc468GPhWRN4G9DY2qeoNvWXVx1Vvfo2LFY2Tm3UpS72NjnY4xxhjTqD2F/1X3ZdpBQ3Xsmnc9wYwBZI23oyPGGGM6lzYLv6o+LiJJwDFu0xpVrfM3ra6rbNkfqSteQb/z/kEgKT3W6RhjjDH7ac+d+/KBtcAjwP8Cn4nIqe0JLiITRWSNiKwTkTubWZ4pIv8SkY9FZIWIXOW2DxORZRGvMhG5yV12j4hsiVh2bru/rc/qy7+g5INfkjr4XNKOviDW6RhjjDEHaM9Q/++As1V1DYCIHAP8HchrbSX3ioBHgLOAImCxiMxW1ZUR3a4HVqrq+SLSF1gjIk+7nzU2Is4W4MWI9R5U1U73pJvit24FDdM7/0E7oc8YY0yn1J7r+BMbij6Aqn4GJLZjvXHAOlXdoKq1wCxgUpM+CmSIUyXTgd1AfZM+ZwDrVfXzdnxmzFRufI3KdS+RNe4uEjMHxzodY4wxplmiqq13EJmJU6CfdJsuBxJU9ao21psMTFTVa935KcB4VZ0W0ScDmA0MBzKAS1T11SZxZgIfqeof3fl7gCuBMmAJcKuqljTz+VOBqQA5OTl5s2bNavV7HoyKigrS0/cdv5dwDf1XTwVJYOuwGRBoz3ZR+2J7xa+4fsbuijn7Gdtyjk5syzk6sbtizn7G9jpuQUHBUlU9odmFqtrqC0gGbgFewBluvxlIbsd6FwOPRsxPAR5u0mcy8CAgwBBgI9AzYnkSsAvIiWjLAYI4oxW/Ama2lUteXp56qbCwcL/53e/+TDc8mKiVmwub7X8osb3iV1w/Y3fFnP2MbTlHJ7blHJ3YXTFnP2N7HRdYoi3UxPYc408A/qCqv4fGY+7J7VivCBgQMZ8LbG3S5ypgupvkOhHZiLP3v8hdfg7O3v72hhUip0Xk/4BX2pGLb2p3r6F0yQP0GH4ZqQPyY5mKMcYY06b2HOOfB6RGzKcC/27HeouBoSIy2L0c8FKcYf1Im3GO4SMiOcAwYEPE8m/jnEjYSET6R8xeBCxvRy6+UFWKC28kkJBG71N+G6s0jDHGmHZrzx5/iqpWNMyoaoWIpLW1kqrWi8g04A2cofmZqrpCRK5zl88A7gUeE5FPcYb771DVXQDuZ5wFfK9J6PtEZCzOeQebmlkeNXvXPEv1F/PpXfAQwR45sUrDGGOMabf2FP69InK8qn4EICJ5QFV7gqvqHGBOk7YZEdNbgbNbWLcS6N1Me6d44k24Zg+7F/6IpJw8Mr7y3VinY4wxxrRLewr/TcA/RKTh+Hx/4BLfMuoiSt77OaGqHeRMegkJBGOdjjHGGNMu7bll72IRGY5z/F2A1Rrnt+xNqvyMsrUz6Dn6OpJzjo91OsYYY0y7tXhyn4h8VUQOA3AL/fHA/wC/E5FeUcqv09FwiF5FDxNM7UvWSffEOh1jjDHmoLR2Vv+fgVoA997804EngD3AX/xPrXMq//RRkivX0OvU+wimZMU6HWOMMeagtDbUH1TV3e70JcBfVPV54HkRWeZ7Zp1UQs8jqcg+i0HDLo11KsYYY8xBa7Xwi0iCqtbjXGs/tZ3rdWtpgydS/HmKPYTHGGNMl9RaAf878JaI7MK5fO9tABEZgjPcb4wxxpgupsXCr6q/EpF5OJfvzXVvqwvOeQE/jEZyxhhjjPFWq0P2qvpBM22f+ZeOMcYYY/zUnnv1G2OMMaabsMJvjDHGxBEr/MYYY0wcscJvjDHGxBEr/MYYY0wcscJvjDHGxBEr/MYYY0wc8bXwi8hEEVkjIutE5M5mlmeKyL9E5GMRWSEiV0Us2yQin4rIMhFZEtHeS0TeFJG17nu2n9/BGGOM6U58K/wiEgQeAc4BRgLfFpGRTbpdD6xU1TFAPs4jf5Milheo6lhVPSGi7U5gnqoOBea588YYY4xpBz/3+McB61R1g6rWArOASU36KJAhzhNv0oHdQH0bcScBj7vTjwMXepaxMcYY083JvlvwexxYZDIwUVWvdeenAONVdVpEnwxgNjAcyAAuUdVX3WUbgRKcjYM/q+pf3PZSVc2KiFGiqgcM94vIVNwnCubk5OTNmjXLs+9WUVFBenq6Z/GiEdty7vqxLefoxLacoxO7K+bsZ2yv4xYUFCxtMlq+j6r68gIuBh6NmJ8CPNykz2TgQUCAIcBGoKe77HD3vR/wMXCqO1/aJEZJW7nk5eWplwoLCz2NF43YlnPXj205Rye25Ryd2F0xZz9jex0XWKIt1EQ/h/qLgAER87nA1iZ9rgJecPNc5xb+4QCqutV93wG8iHPoAGC7iPQHcN93+PYNjDHGmG7Gz8K/GBgqIoPdE/YuxRnWj7QZOANARHKAYcAGEenhHgZARHoAZwPL3XVmA1e401cAL/v4HYwxxphupdXH8h4KVa0XkWnAG0AQmKmqK0TkOnf5DOBe4DER+RRnuP8OVd0lIkcBLzrn/JEAPKOqr7uhpwPPicg1OBsOF/v1HYwxxpjuxrfCD6Cqc4A5TdpmRExvxdmbb7reBmBMCzGLcUcJjDHGGHNw7M59xhhjTByxwm+MMcbEESv8xhhjTByxwm+MMcbEESv8xhhjTByxwm+MMcbEESv8xhhjTByxwm+MMcbEESv8xhhjTByxwm+MMcbEESv8xhhjTByxwm+MMcbEESv8xhhjTByxwm+MMcbEESv8xhhjTByxwm+MMcbEEV8Lv4hMFJE1IrJORO5sZnmmiPxLRD4WkRUicpXbPkBECkVkldt+Y8Q694jIFhFZ5r7O9fM7GGOMMd1Jgl+BRSQIPAKcBRQBi0VktqqujOh2PbBSVc8Xkb7AGhF5GqgHblXVj0QkA1gqIm9GrPugqj7gV+7GGGNMd+XnHv84YJ2qblDVWmAWMKlJHwUyRESAdGA3UK+q21T1IwBVLQdWAUf4mKsxxhgTF0RV/QksMhmYqKrXuvNTgPGqOi2iTwYwGxgOZACXqOqrTeIMAhYCo1S1TETuAa4EyoAlOCMDJc18/lRgKkBOTk7erFmzPPtuFRUVpKenexYvGrEt564f23KOTmzLOTqxu2LOfsb2Om5BQcFSVT2h2YWq6ssLuBh4NGJ+CvBwkz6TgQcBAYYAG4GeEcvTgaXANyPacoAgzmjFr4CZbeWSl5enXiosLPQ0XjRiW85dP7blHJ3YlnN0YnfFnP2M7XVcYIm2UBP9HOovAgZEzOcCW5v0uQp4wc1znVv4hwOISCLwPPC0qr7QsIKqblfVkKqGgf/DOaRgjDHGmHbws/AvBoaKyGARSQIuxRnWj7QZOANARHKAYcAG95j/X4FVqvr7yBVEpH/E7EXAcp/yN8YYY7od387qV9V6EZkGvIEzND9TVVeIyHXu8hnAvcBjIvIpznD/Haq6S0S+hnNo4FMRWeaGvFtV5wD3ichYnBMDNwHf8+s7mAMV761l5fZyVu2oYOX2ctbsqKC0pJLRuz4mNzOV3MwUcrOc9yMyU+iZkhjrlI0xxkTwrfADuIV6TpO2GRHTW4Gzm1nvHZwNgeZiTvE4TdOEqrK1rJpV2ysaC/zq7c77zr21jf3SEoMM75dORY0ye8V2tpfXHBArIznB3RhI4YgmGwa5mankZqWQnZqIM8jj3/epCylVdSEq60JU1obYXRNGVX39XGOM6Yx8LfymcwuHlU0llaxyi/qqHRWsct/Lqusb+2WlJjIyJ53zjz2MkTnpjOiXzsicDAZkpRIICAsWLCA/P5+a+hDbymr4orSKoj3VFLnvW/ZUUVRazYovd7KtvJqmF5KkJgb2Gy04wt0o2Lm1jq0fFVFVF6ayLuQU7trQvum6ENV1YSpr61vtU1UXJhQ+8OqVlMI5DMxO5cjsVAZmpzEwy5l2XmnkZqaQELSbWxpjuhcr/HGgLhTm8/IQL3y6bb+99zU7K6iqCzf2OywjmRH90rn8+Fy3wGcwMiednIzkdu0ZJycEGdQrjUG90lrN5cvymsaNgiJ3o2CLO/3W+mK2llVT31CoP/pPM58TIC0xSGpikLSkIKmJznxaYpCe6cluW8Mr0Djf0Cc1MchHy1eR1CeXzSWVfF5SxSvbDhyxCAgckZnCkdlp7sZBauP0kdmpDMxKJS3JfoWMMV2L/dXqRvbW1LNmZ4W7517B6h3lrNxewbpde51C+tYSAI7MTmVEv3QKhvRhRI6z9z6iXzrZaUm+55gYDDAgK5UBWakt9gmFlR0VNcxZ8C4TThy3r8gnBklJDBIMHPrw/JF715OfP3K/tqq6EF+UVvF5SRWfuxsEm93pdzbupmhZ9QEjB317JO03apCbmcLnG2tZkbgRQRBxjlmJ0Ljx1Di/33KJ6Nf8sk3b6+m9rYyBWalkpnbucydUlZKqOr6sDLOzoqbx3zDgwb+dMebQWOHvgnZX1rrH38vdAu/swX9eUtXYJxgQhvROY0ROBheOOgx2F/Ff+V9lWN900pM79z97MCD075nC0T2DDO+XEbXPTU0MckzfdI7p2/xNNOpDYbaWVbsbBvtvHKz4spzXVu/YN4Kywp+LTe5e/BYAPVMSGOhuQA3IckYjBma5r2zncEmiT4cpQmFle3kNRXuq3JEaZ8Rmy37zVft+FvPnNq6bkhCgR5IzUpOWuO+9R1JC43RqY1tEP7dvj4j11u0JMXh3JdlpiWQkJ9j5Gsa0U+euAHGs6Ql2q7aXNxb4HRX7TrBLSQgwvF86Jw/qxdXjnOPvI3IyGNqnB0kJ+/7wL1iwnbzcrBh8k+4jIRhwzgXITuOUZparKqVVdbz19jucfPIE1G1z3t0+KKrsW9Y43cyyiHaA+e8uou9RI9hcUsXm0iq+KHU2OhZ/UcquiJMuwRk1OLxnirNR0HTjwH3vlXbgSZXVdSG2llU7h1/KnPM0tpRVsyViflt5zQEjH4lB4YiezpUcebmZnD8yh9ysFLZtWs/Ao4Y2nlRZWRdib22IqtrQfm1lNfV8WV7D3tr6/drrQm3cWfTteYCzsZiVkkB2WhLZqYnOKy2RrIZpd75X5HK3zTYaTLyxwt9J7K2p54mlRcxeVsVdn7x9wAl2mSkJjMjJ4BsjcxjRL8Mt8OkcmZ3mydC3OXQiQnZaElnJAfplJHse/8vsIPljDm92WWVtPUV7qhs3CiI3Dv6zZQ8vr/iSmvrwfuukJgYYmJXK4T1T+GLnXkoL3zhgAwIgPTlIbqYzinDG0D4c3nBFhnvJZm5mKn16JDU7jL8g/AX5Xxvc4e9cFwo3nrC5N2JjYW9tiHeX/Icjjh7G7so6SqpqKamso7SqjpKqOkoq69i4u9KZrqpr9uTOBk03GsJVlfRfv4jUxAApiUGSEwKkJgZJSXDmU1qaTww4bQnBFudrQ0p5dT21oTA19WFqQ+6rPkyN++60aTNtYWrrlZpQiNp63W/dTZurea1iJYnBAAkBITEoJAYDJAaFhECAxMC++f36BJq2BRrbG/ps3Rtm0+5KBAgEnMNOAREC7uGrgHt4KiD7ljXM79eH/ecjacQZv/s2kltYvl97xLS7RBVqQkplbT2qEHY3nsPqxGmcDzuxwu4GeNjd2A6H9210N7ZF9NlYHqL3tjLCqoTCTrym0878vumw4i53p1UJR66ryhfb68lv7y/GIbLCH2MllbX88d1NPPT2Boor6+iVLIzJDfKd43MZ4Z5gNyInncPaeYKdiU9pSQmtHqZQVXZW1O4bKXA3Dr4orWJrWTV9UoTTj+rvXHbZ0726oqdzGWYs78XgFKJAszkEtiSSP25gmzFUlYqakLNx4G4U7HbfnQ2D/TcavqhQtpQ5hyqq60JU14eprnc2QJpuPHXIa68degyXCCQHA6BhdPNG6sPa6kZOhxXO8z4mwCv/8icuePpzPsBbb3keckjPAAc8u94nVvhjZHt5Db9/az1/en8TFTUhzhuRw51nDKF20yfk558c6/RMNyMi9MtIpl9GMicMyDpguXNJ5ujoJxYFIkJGSgIZKQkMzG67v/OzOK3ZZeGws6ddXe9sFFTVhamuD7U9Xxemqj7E5k0bGTZkCEkJAZKCQnJCkKSgkBQMkJQQIDkh4Ew3vBKkmbZ9/RpG+xouqW3IsT6s1IXCzns4TF3Ima8L7VtWF45si+gTuW5IWbFyFcOGD292z7g9e9MHruf02bhxE4MGDYr4d4r4N3Nv47Jf237L9//3bdq+ceMGjj7q6HaNSkSOZLTYJ6J99aqVfGXUse6IhhAMOPGbTgcEd16amYdgQ3vA6b9syaK2/3N6xAp/lH2+u5L7F6xn5qLN1IbCfGvM4dxx+hDGHJ4JwIJNsc3PGNOyQEBICThXl9CBKysWLNhKfv7RPmS2TyAgJAVkv3N8DsWC8nXkf3VA2x0PNu6CbeTnD/M8rhN7C/n5Q/yJvWct+aObP+R2KLamRu+eIVb4o2T1jnJ+O38dT3+0BRH47xMG8KP8oxnawtCsMcYY4wcr/D5bWlTK9HnreGH5NlISAlw/YRC3nnY0ua1cx26MMcb4xQq/TxauL+Y389fyxpqdZKYkcNfpQ7nxlMH0Tff+bG9jjDGmvazwe0hVeX31Dn4zfx3vbNxN3x5J/Prc4Xz/pEGd/k5rxhhj4oMVfg+EwsoLn27jN/PWsmxrGQOyUnjowlFcPW6A3cvdGGNMp2JV6RDU1od5+qMiflu4js927uWYvj3467fGcPnxuZ6dUWuMMcZ4yQp/B9SElIff2cgDC9bxRWk1Yw/vybNT8vjmV/rbXfSMMcZ0ar7ulorIRBFZIyLrROSAmxKJSKaI/EtEPhaRFSJyVVvrikgvEXlTRNa67+24JYd3Hl/8BZfN28uNLy1nYFYqr14zjqU3n8rFYw63om+MMabT863wi0gQeAQ4BxgJfFtERjbpdj2wUlXHAPnA70QkqY117wTmqepQYJ47HzXBgDAkM8BbPziZt6d9jXNG5NitdI0xxnQZfu7xjwPWqeoGVa0FZgGTmvRRIEOcypkO7Abq21h3EvC4O/04cKGP3+EAlx9/BL8dn8YpR/WO5scaY4wxnpDIpx55GlhkMjBRVa9156cA41V1WkSfDGA2MBzIAC5R1VdbW1dESlU1KyJGiaoeMNwvIlOBqQA5OTl5s2bN8uy7VVRUkJ7uzx33/IptOXf92JZzdGJbztGJ3RVz9jO213ELCgqWquoJzS50ngnu/Qu4GHg0Yn4K8HCTPpOBB3GerTAE2Aj0bG1doLRJjJK2csnLy1MvFRYWehovGrEt564f23KOTmzLOTqxu2LOfsb2Oi6wRFuoiX4O9RcBkU92yAW2NulzFfCCm+c6t/APb2Pd7SLSH8B93+FD7sYYY0y35GfhXwwMFZHBIpIEXIozrB9pM3AGgIjkAMOADW2sOxu4wp2+AnjZx+9gjDHGdCu+XcevqvUiMg14AwgCM1V1hYhc5y6fAdwLPCYin+IM99+hqrsAmlvXDT0deE5ErsHZcLjYr+9gjDHGdDe+3sBHVecAc5q0zYiY3gqc3d513fZi3FECY4wxxhwcu6+sMcYYE0es8BtjjDFxxLfr+DsTEdkJfO5hyD7ALg/jRSO25dz1Y1vO0YltOUcndlfM2c/YXsc9UlX7NrcgLgq/10RkibZ0Y4ROGtty7vqxLefoxLacoxO7K+bsZ2w/c27KhvqNMcaYOGKF3xhjjIkjVvg75i9dMLbl3PVjW87RiW05Ryd2V8zZz9h+5rwfO8ZvjDHGxBHb4zfGGGPiiBV+Y4wxJo5Y4T8IIjJTRHaIyHKP4w4QkUIRWSUiK0TkRg9jp4jIIhH52I39C69iu/GDIvIfEXnF47ibRORTEVkmIks8jp0lIv8UkdXuz/wkD2IOc3NteJWJyE0epIuI3Oz+2y0Xkb+LSIoXcd3YN7pxVxxqvs39fohILxF5U0TWuu/ZHsW92M05LCIdvgSqhdj3u/83PhGRF0Uky8PY97pxl4nIXBE53Iu4EctuExEVkT4e5nyPiGyJ+L99rlc5i8gPRWSN+295n4c5PxuR7yYRWeZR3LEi8kHD3yURGedhzmNE5H33796/RKRnR2K3S0vP67XXgS/gVOB4YLnHcfsDx7vTGcBnwEiPYguQ7k4nAh8CJ3qY+y3AM8ArHv9MNgF9fPp3fBy41p1OArI8jh8EvsS5gcahxjoC53HVqe78c8CVHuU5ClgOpOE8t+PfwNBDiHfA7wdwH3CnO30n8FuP4o7AeZrnAuAEj3M+G0hwp3/bkZxbid0zYvoGYIYXcd32ATgPNvu8o787LeR8D3DbIf5fay5ugft/Ltmd7+dV7CbLfwf8zKOc5wLnuNPnAgs8/HksBk5zp68G7j2Un3lrL9vjPwiquhDY7UPcbar6kTtdDqzC+YPvRWxV1Qp3NtF9eXJGp4jkAt8AHvUiXjS4W9GnAn8FUNVaVS31+GPOANarqld3i0wAUkUkAadIb/Uo7gjgA1WtVNV64C3goo4Ga+H3YxLOhhbu+4VexFXVVaq6pgNptif2XPfnAfABkOth7LKI2R504Hexlb9DDwI/6kjMdsQ+JC3E/T4wXVVr3D47PIwNgIgI8C3g7x7FVaBhTzyTDv4uthB7GLDQnX4T+K+OxG4PK/ydjIgMAo7D2TP3KmbQHeraAbypql7F/n84f2jCHsWLpMBcEVkqIlM9jHsUsBP4m3uI4lER6eFhfIBL6cAfmuao6hbgAZxHUG8D9qjqXC9i4+ztnyoivUUkDWcPZoBHsRvkqOo2cDZwgX4ex/fb1cBrXgYUkV+JyBfA5cDPPIp5AbBFVT/2Il4zprmHKGZ25HBNC44BThGRD0XkLRH5qkdxI50CbFfVtR7Fuwm43/33ewC4y6O44Pw+XuBOX4z3v4uNrPB3IiKSDjwP3NRkz+CQqGpIVcfi7LmME5FRhxpTRM4Ddqjq0kON1YIJqno8cA5wvYic6lHcBJwhtj+p6nHAXpwhaE+ISBLOL+8/PIqXjbPXPBg4HOghIt/xIraqrsIZyn4TeB34GKhvdaU4IiI/xvl5PO1lXFX9saoOcONOO9R47kbbj/FoI6IZfwKOBsbibHz+zqO4CUA2cCJwO/Ccu4fupW/j0Ua46/vAze6/3824I4ceuRrnb91SnEO+tR7G3o8V/k5CRBJxiv7TqvqCH5/hDmkvACZ6EG4CcIGIbAJmAaeLyFMexAVAVbe67zuAF4EOnUTTjCKgKGLU4584GwJeOQf4SFW3exTvTGCjqu5U1TrgBeBkj2Kjqn9V1eNV9VScoUev9owabBeR/gDue4eGc6NNRK4AzgMuV/egqw+ewZvh3KNxNgw/dn8fc4GPROQwD2KjqtvdnYcw8H94+7v4gns4chHOyGGHTkpsjnto7JvAs17FBK7A+R0EZ+Peq58FqrpaVc9W1TycjZX1XsVuygp/J+Bu5f4VWKWqv/c4dt+Gs5JFJBWnkKw+1Liqepeq5qrqIJyh7fmq6smeqIj0EJGMhmmck608uZJCVb8EvhCRYW7TGcBKL2K7vN7D2AycKCJp7v+TM3DOAfGEiPRz3wfi/JH0MneA2Th/LHHfX/Y4vudEZCJwB3CBqlZ6HHtoxOwFePO7+Kmq9lPVQe7vYxHOycJfHmpsaNxga3ARHv0uAi8Bp7ufcQzOibZePp3uTGC1qhZ5GHMrcJo7fToebihH/C4GgJ8AM7yKfQC/zhrsji+cP4rbgDqcX65rPIr7NZxj2p8Ay9zXuR7FHg38x429nA6c3dqOz8jHw7P6cY7Df+y+VgA/9jjfscAS92fyEpDtUdw0oBjI9DjfX+AUiOXAk7hnQXsU+22cDZ+PgTMOMdYBvx9Ab2Aezh/IeUAvj+Je5E7XANuBNzzMeR3wRcTv4kGfed9K7Ofdf8dPgH8BR3gRt8nyTXT8rP7mcn4S+NTNeTbQ36O4ScBT7s/jI+B0r3J22x8DrvP4//PXgKXu78uHQJ6HsW/EuaLrM2A67p11/XjZLXuNMcaYOGJD/cYYY0wcscJvjDHGxBEr/MYYY0wcscJvjDHGxBEr/MYYY0wcscJvjDHGxBEr/MaYqHAfj9rRx8VeGfkI20OJZUy8s8JvjOkKrsR5VoEx5hBZ4TcmzojIIBFZ7T6ZcLmIPC0iZ4rIuyKyVkTGua/33CcYvtdwi2MRuUVEZrrTX3HXT2vhc3qLyFw3xp8BiVj2HRFZJCLLROTPIhJ02ytE5Hci8pGIzHNvOT0ZOAF42u2f6ob5odvvUxEZ7ufPzJjuxAq/MfFpCPAHnFs6Dwcuw7kd6W3A3Ti3CD5VnScY/gz4tbve/wOGiMhFwN+A72nL97P/OfCOG2M2MBBAREYAl+A8gXEsEMJ5RC04z6j/SJ0nM74F/FxV/4lzi+XLVXWsqla5fXe5/f7k5m2MaYeEWCdgjImJjar6KYCIrADmqaqKyKfAICATeNx9qIwCiQCqGhaRK3Hu2/5nVX23lc84FefBP6jqqyJS4rafAeQBi92nsKay76l9YfY9Te0p9j0JrTkNy5Y2fI4xpm1W+I2JTzUR0+GI+TDO34V7gUJVvUhEBuE8zrnBUKCC9h1zb+5hIAI8rqp3dXD9Bg05h7C/Zca0mw31G2OakwlscaevbGgUkUycQwSnAr3d4+8tWYg7hC8i5wDZbvs8YHLEY0h7iciR7rIA0BDzMuAdd7ocyDiE72OMcVnhN8Y05z7gNyLyLhCMaH8Q+F9V/QznUaLTGwp4M34BnCoiHwFnA5sBVHUlzvPG54rIJ8CbQMMz3/cCx4rIUpznnf/SbX8MmNHk5D5jTAfYY3mNMZ2GiFSoanqs8zCmO7M9fmOMMSaO2B6/MeaQiMhVwI1Nmt9V1etjkY8xpnVW+I0xxpg4YkP9xhhjTByxwm+MMcbEESv8xhhjTByxwm+MMcbEkf8PFwgoVfqLZf4AAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"#### Desafio 12 - Escolha um modelo de ML, estude os hiperparâmetros desse modelo e teste vários deles.\n\nJuntei com o desafio 10. "},{"metadata":{},"cell_type":"markdown","source":"#### Desafio 13 - Pesquisar como salvar um modelo treinado.\n\nPara salvar e dar load num modelo treinado anteriormente, podemos utilizar 2 libs diferentes:\n* Pickle API\n* Joblib API\n\nAmbas funcionam de forma parecida, serializando e salvando objetos de Python. No entanto, a última é focada em salvar objetos que usam NumPy arrays de forma extremamente eficiente, sendo bem útil para algoritmos que guardam o dataset inteiro para fazer previsões (como é o caso do K-NN).\n<br>\n\nEm ambas as APIs, os métodos são bem similares:\n* O objeto é salvo utilizando: \n    * `joblib.dump(model, \"filename.sav\")`\n    * `pickle.dump(open(\"filename.sav\", \"wb\"))`\n* E é carregado para um novo projeto utilizando: \n    * `joblib.load(\"filename.sav\")`\n    * `pickle.load(open(\"filename.sav\", \"rb\"))`\n    \nAlgumas considerações devem ser tomadas ao finalizar e salvar modelos de ML, como tomar nota da versão do Python e das Libs que estão sendo utilizadas"},{"metadata":{},"cell_type":"markdown","source":"## Aula 06 - Finalizando a validação"},{"metadata":{},"cell_type":"markdown","source":"Um número elevado de features nem sempre significa um melhor modelo. Na maioria das vezes, na verdade, features em excesso causam danos ao modelo (seja dano em perfomance ou em custo de tempo). \n<br>\n\nExistem diversas maneiras de selecionar e/ou cortar features e uma delas é **cortar as colunas com alta correlação**. Enquanto a alta correlação (positiva ou negativa) pode afetar a perfomance de modelos lineares, modelos baseados em árvore não são atingidos (ainda assim, são consideradas **boas práticas** realizar a remoção dessas colunas)."},{"metadata":{"trusted":true},"cell_type":"code","source":"df_limpo.reset_index(drop = True, inplace = True)\n\ndf_limpo","execution_count":76,"outputs":[{"output_type":"execute_result","execution_count":76,"data":{"text/plain":"     PATIENT_VISIT_IDENTIFIER  AGE_ABOVE65  AGE_PERCENTIL  GENDER  \\\n0                           0            1              5       0   \n1                           1            1              8       1   \n2                           2            0              0       0   \n3                           3            0              3       1   \n4                           4            0              0       0   \n..                        ...          ...            ...     ...   \n379                       380            0              3       1   \n380                       381            1              9       0   \n381                       382            0              4       0   \n382                       383            0              3       1   \n383                       384            0              4       1   \n\n     DISEASE GROUPING 1  DISEASE GROUPING 2  DISEASE GROUPING 3  \\\n0                   0.0                 0.0                 0.0   \n1                   0.0                 0.0                 0.0   \n2                   0.0                 0.0                 0.0   \n3                   0.0                 0.0                 0.0   \n4                   0.0                 0.0                 0.0   \n..                  ...                 ...                 ...   \n379                 0.0                 0.0                 0.0   \n380                 0.0                 0.0                 0.0   \n381                 0.0                 0.0                 0.0   \n382                 0.0                 0.0                 0.0   \n383                 0.0                 0.0                 0.0   \n\n     DISEASE GROUPING 4  DISEASE GROUPING 5  DISEASE GROUPING 6  ...  \\\n0                   0.0                 1.0                 1.0  ...   \n1                   0.0                 0.0                 0.0  ...   \n2                   0.0                 0.0                 0.0  ...   \n3                   0.0                 0.0                 0.0  ...   \n4                   0.0                 0.0                 0.0  ...   \n..                  ...                 ...                 ...  ...   \n379                 0.0                 0.0                 0.0  ...   \n380                 0.0                 0.0                 0.0  ...   \n381                 0.0                 0.0                 0.0  ...   \n382                 0.0                 0.0                 0.0  ...   \n383                 0.0                 0.0                 0.0  ...   \n\n     TEMPERATURE_DIFF  OXYGEN_SATURATION_DIFF  \\\n0           -1.000000               -1.000000   \n1           -1.000000               -1.000000   \n2           -1.000000               -0.959596   \n3           -1.000000               -1.000000   \n4           -0.952381               -0.979798   \n..                ...                     ...   \n379         -1.000000               -1.000000   \n380         -0.619048               -1.000000   \n381         -1.000000               -1.000000   \n382         -1.000000               -1.000000   \n383         -1.000000               -1.000000   \n\n     BLOODPRESSURE_DIASTOLIC_DIFF_REL  BLOODPRESSURE_SISTOLIC_DIFF_REL  \\\n0                           -1.000000                        -1.000000   \n1                           -1.000000                        -1.000000   \n2                           -0.515528                        -0.351328   \n3                           -1.000000                        -1.000000   \n4                           -1.000000                        -0.883669   \n..                                ...                              ...   \n379                         -1.000000                        -1.000000   \n380                         -1.000000                        -1.000000   \n381                         -1.000000                        -1.000000   \n382                         -1.000000                        -1.000000   \n383                         -1.000000                        -1.000000   \n\n     HEART_RATE_DIFF_REL  RESPIRATORY_RATE_DIFF_REL  TEMPERATURE_DIFF_REL  \\\n0              -1.000000                  -1.000000             -1.000000   \n1              -1.000000                  -1.000000             -1.000000   \n2              -0.747001                  -0.756272             -1.000000   \n3              -1.000000                  -1.000000             -1.000000   \n4              -0.956805                  -0.870968             -0.953536   \n..                   ...                        ...                   ...   \n379            -1.000000                  -1.000000             -1.000000   \n380            -1.000000                  -1.000000             -0.612627   \n381            -1.000000                  -1.000000             -1.000000   \n382            -1.000000                  -1.000000             -1.000000   \n383            -1.000000                  -1.000000             -1.000000   \n\n     OXYGEN_SATURATION_DIFF_REL  WINDOW  ICU  \n0                     -1.000000     0-2    1  \n1                     -1.000000     0-2    1  \n2                     -0.961262     0-2    1  \n3                     -1.000000     0-2    0  \n4                     -0.980333     0-2    0  \n..                          ...     ...  ...  \n379                   -1.000000     0-2    1  \n380                   -1.000000     0-2    0  \n381                   -1.000000     0-2    1  \n382                   -1.000000     0-2    0  \n383                   -1.000000     0-2    0  \n\n[384 rows x 231 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PATIENT_VISIT_IDENTIFIER</th>\n      <th>AGE_ABOVE65</th>\n      <th>AGE_PERCENTIL</th>\n      <th>GENDER</th>\n      <th>DISEASE GROUPING 1</th>\n      <th>DISEASE GROUPING 2</th>\n      <th>DISEASE GROUPING 3</th>\n      <th>DISEASE GROUPING 4</th>\n      <th>DISEASE GROUPING 5</th>\n      <th>DISEASE GROUPING 6</th>\n      <th>...</th>\n      <th>TEMPERATURE_DIFF</th>\n      <th>OXYGEN_SATURATION_DIFF</th>\n      <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n      <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n      <th>HEART_RATE_DIFF_REL</th>\n      <th>RESPIRATORY_RATE_DIFF_REL</th>\n      <th>TEMPERATURE_DIFF_REL</th>\n      <th>OXYGEN_SATURATION_DIFF_REL</th>\n      <th>WINDOW</th>\n      <th>ICU</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-0.959596</td>\n      <td>-0.515528</td>\n      <td>-0.351328</td>\n      <td>-0.747001</td>\n      <td>-0.756272</td>\n      <td>-1.000000</td>\n      <td>-0.961262</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-0.952381</td>\n      <td>-0.979798</td>\n      <td>-1.000000</td>\n      <td>-0.883669</td>\n      <td>-0.956805</td>\n      <td>-0.870968</td>\n      <td>-0.953536</td>\n      <td>-0.980333</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>379</th>\n      <td>380</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>380</th>\n      <td>381</td>\n      <td>1</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-0.619048</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-0.612627</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>381</th>\n      <td>382</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>382</th>\n      <td>383</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>383</th>\n      <td>384</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>384 rows × 231 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Criando a matriz de correlação apenas dos dados contínuos\nmatriz_corr = df_limpo.iloc[:, 4:-2].corr().abs()\n\nmatriz_corr","execution_count":82,"outputs":[{"output_type":"execute_result","execution_count":82,"data":{"text/plain":"                                 DISEASE GROUPING 1  DISEASE GROUPING 2  \\\nDISEASE GROUPING 1                         1.000000            0.232833   \nDISEASE GROUPING 2                         0.232833            1.000000   \nDISEASE GROUPING 3                         0.259372            0.072901   \nDISEASE GROUPING 4                         0.018628            0.107559   \nDISEASE GROUPING 5                         0.347914            0.152228   \n...                                             ...                 ...   \nBLOODPRESSURE_SISTOLIC_DIFF_REL            0.052073            0.028948   \nHEART_RATE_DIFF_REL                        0.162467            0.224602   \nRESPIRATORY_RATE_DIFF_REL                  0.019039            0.146439   \nTEMPERATURE_DIFF_REL                       0.078430            0.221817   \nOXYGEN_SATURATION_DIFF_REL                 0.049045            0.267425   \n\n                                 DISEASE GROUPING 3  DISEASE GROUPING 4  \\\nDISEASE GROUPING 1                         0.259372            0.018628   \nDISEASE GROUPING 2                         0.072901            0.107559   \nDISEASE GROUPING 3                         1.000000            0.300146   \nDISEASE GROUPING 4                         0.300146            1.000000   \nDISEASE GROUPING 5                         0.275185            0.127296   \n...                                             ...                 ...   \nBLOODPRESSURE_SISTOLIC_DIFF_REL            0.017722            0.030982   \nHEART_RATE_DIFF_REL                        0.018194            0.030845   \nRESPIRATORY_RATE_DIFF_REL                  0.026603            0.013118   \nTEMPERATURE_DIFF_REL                       0.005393            0.014426   \nOXYGEN_SATURATION_DIFF_REL                 0.011904            0.024363   \n\n                                 DISEASE GROUPING 5  DISEASE GROUPING 6  \\\nDISEASE GROUPING 1                         0.347914            0.199194   \nDISEASE GROUPING 2                         0.152228            0.031235   \nDISEASE GROUPING 3                         0.275185            0.268365   \nDISEASE GROUPING 4                         0.127296            0.173474   \nDISEASE GROUPING 5                         1.000000            0.129764   \n...                                             ...                 ...   \nBLOODPRESSURE_SISTOLIC_DIFF_REL            0.057854            0.001202   \nHEART_RATE_DIFF_REL                        0.055391            0.004998   \nRESPIRATORY_RATE_DIFF_REL                  0.006599            0.048619   \nTEMPERATURE_DIFF_REL                       0.041944            0.006971   \nOXYGEN_SATURATION_DIFF_REL                 0.012808            0.033638   \n\n                                      HTN  IMMUNOCOMPROMISED     OTHER  \\\nDISEASE GROUPING 1               0.464991           0.187721  0.128779   \nDISEASE GROUPING 2               0.140774           0.122963  0.069931   \nDISEASE GROUPING 3               0.470774           0.067851  0.140691   \nDISEASE GROUPING 4               0.129277           0.102198  0.061509   \nDISEASE GROUPING 5               0.437339           0.101896  0.147387   \n...                                   ...                ...       ...   \nBLOODPRESSURE_SISTOLIC_DIFF_REL  0.017899           0.042838  0.036087   \nHEART_RATE_DIFF_REL              0.110824           0.024410  0.049761   \nRESPIRATORY_RATE_DIFF_REL        0.010122           0.006205  0.033245   \nTEMPERATURE_DIFF_REL             0.085658           0.019229  0.006927   \nOXYGEN_SATURATION_DIFF_REL       0.057847           0.074031  0.014101   \n\n                                 ALBUMIN_MEDIAN  ...  HEART_RATE_DIFF  \\\nDISEASE GROUPING 1                     0.013398  ...         0.188455   \nDISEASE GROUPING 2                     0.037642  ...         0.227727   \nDISEASE GROUPING 3                     0.009800  ...         0.022354   \nDISEASE GROUPING 4                     0.035429  ...         0.022661   \nDISEASE GROUPING 5                     0.046343  ...         0.094562   \n...                                         ...  ...              ...   \nBLOODPRESSURE_SISTOLIC_DIFF_REL        0.062668  ...         0.315267   \nHEART_RATE_DIFF_REL                    0.030320  ...         0.972805   \nRESPIRATORY_RATE_DIFF_REL              0.008773  ...         0.523308   \nTEMPERATURE_DIFF_REL                   0.021769  ...         0.645859   \nOXYGEN_SATURATION_DIFF_REL             0.063720  ...         0.589067   \n\n                                 RESPIRATORY_RATE_DIFF  TEMPERATURE_DIFF  \\\nDISEASE GROUPING 1                            0.042852          0.076589   \nDISEASE GROUPING 2                            0.159572          0.219816   \nDISEASE GROUPING 3                            0.031549          0.006847   \nDISEASE GROUPING 4                            0.005775          0.013765   \nDISEASE GROUPING 5                            0.021797          0.040025   \n...                                                ...               ...   \nBLOODPRESSURE_SISTOLIC_DIFF_REL               0.339359          0.329350   \nHEART_RATE_DIFF_REL                           0.558114          0.686505   \nRESPIRATORY_RATE_DIFF_REL                     0.973030          0.539410   \nTEMPERATURE_DIFF_REL                          0.539246          0.999628   \nOXYGEN_SATURATION_DIFF_REL                    0.522798          0.701965   \n\n                                 OXYGEN_SATURATION_DIFF  \\\nDISEASE GROUPING 1                             0.049611   \nDISEASE GROUPING 2                             0.259062   \nDISEASE GROUPING 3                             0.012592   \nDISEASE GROUPING 4                             0.025052   \nDISEASE GROUPING 5                             0.012239   \n...                                                 ...   \nBLOODPRESSURE_SISTOLIC_DIFF_REL                0.432987   \nHEART_RATE_DIFF_REL                            0.629669   \nRESPIRATORY_RATE_DIFF_REL                      0.517766   \nTEMPERATURE_DIFF_REL                           0.706897   \nOXYGEN_SATURATION_DIFF_REL                     0.999488   \n\n                                 BLOODPRESSURE_DIASTOLIC_DIFF_REL  \\\nDISEASE GROUPING 1                                       0.048554   \nDISEASE GROUPING 2                                       0.073528   \nDISEASE GROUPING 3                                       0.005903   \nDISEASE GROUPING 4                                       0.043890   \nDISEASE GROUPING 5                                       0.018934   \n...                                                           ...   \nBLOODPRESSURE_SISTOLIC_DIFF_REL                          0.776732   \nHEART_RATE_DIFF_REL                                      0.321500   \nRESPIRATORY_RATE_DIFF_REL                                0.365927   \nTEMPERATURE_DIFF_REL                                     0.289099   \nOXYGEN_SATURATION_DIFF_REL                               0.405593   \n\n                                 BLOODPRESSURE_SISTOLIC_DIFF_REL  \\\nDISEASE GROUPING 1                                      0.052073   \nDISEASE GROUPING 2                                      0.028948   \nDISEASE GROUPING 3                                      0.017722   \nDISEASE GROUPING 4                                      0.030982   \nDISEASE GROUPING 5                                      0.057854   \n...                                                          ...   \nBLOODPRESSURE_SISTOLIC_DIFF_REL                         1.000000   \nHEART_RATE_DIFF_REL                                     0.349502   \nRESPIRATORY_RATE_DIFF_REL                               0.386949   \nTEMPERATURE_DIFF_REL                                    0.323661   \nOXYGEN_SATURATION_DIFF_REL                              0.429762   \n\n                                 HEART_RATE_DIFF_REL  \\\nDISEASE GROUPING 1                          0.162467   \nDISEASE GROUPING 2                          0.224602   \nDISEASE GROUPING 3                          0.018194   \nDISEASE GROUPING 4                          0.030845   \nDISEASE GROUPING 5                          0.055391   \n...                                              ...   \nBLOODPRESSURE_SISTOLIC_DIFF_REL             0.349502   \nHEART_RATE_DIFF_REL                         1.000000   \nRESPIRATORY_RATE_DIFF_REL                   0.555210   \nTEMPERATURE_DIFF_REL                        0.689311   \nOXYGEN_SATURATION_DIFF_REL                  0.623838   \n\n                                 RESPIRATORY_RATE_DIFF_REL  \\\nDISEASE GROUPING 1                                0.019039   \nDISEASE GROUPING 2                                0.146439   \nDISEASE GROUPING 3                                0.026603   \nDISEASE GROUPING 4                                0.013118   \nDISEASE GROUPING 5                                0.006599   \n...                                                    ...   \nBLOODPRESSURE_SISTOLIC_DIFF_REL                   0.386949   \nHEART_RATE_DIFF_REL                               0.555210   \nRESPIRATORY_RATE_DIFF_REL                         1.000000   \nTEMPERATURE_DIFF_REL                              0.537379   \nOXYGEN_SATURATION_DIFF_REL                        0.513188   \n\n                                 TEMPERATURE_DIFF_REL  \\\nDISEASE GROUPING 1                           0.078430   \nDISEASE GROUPING 2                           0.221817   \nDISEASE GROUPING 3                           0.005393   \nDISEASE GROUPING 4                           0.014426   \nDISEASE GROUPING 5                           0.041944   \n...                                               ...   \nBLOODPRESSURE_SISTOLIC_DIFF_REL              0.323661   \nHEART_RATE_DIFF_REL                          0.689311   \nRESPIRATORY_RATE_DIFF_REL                    0.537379   \nTEMPERATURE_DIFF_REL                         1.000000   \nOXYGEN_SATURATION_DIFF_REL                   0.701367   \n\n                                 OXYGEN_SATURATION_DIFF_REL  \nDISEASE GROUPING 1                                 0.049045  \nDISEASE GROUPING 2                                 0.267425  \nDISEASE GROUPING 3                                 0.011904  \nDISEASE GROUPING 4                                 0.024363  \nDISEASE GROUPING 5                                 0.012808  \n...                                                     ...  \nBLOODPRESSURE_SISTOLIC_DIFF_REL                    0.429762  \nHEART_RATE_DIFF_REL                                0.623838  \nRESPIRATORY_RATE_DIFF_REL                          0.513188  \nTEMPERATURE_DIFF_REL                               0.701367  \nOXYGEN_SATURATION_DIFF_REL                         1.000000  \n\n[225 rows x 225 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>DISEASE GROUPING 1</th>\n      <th>DISEASE GROUPING 2</th>\n      <th>DISEASE GROUPING 3</th>\n      <th>DISEASE GROUPING 4</th>\n      <th>DISEASE GROUPING 5</th>\n      <th>DISEASE GROUPING 6</th>\n      <th>HTN</th>\n      <th>IMMUNOCOMPROMISED</th>\n      <th>OTHER</th>\n      <th>ALBUMIN_MEDIAN</th>\n      <th>...</th>\n      <th>HEART_RATE_DIFF</th>\n      <th>RESPIRATORY_RATE_DIFF</th>\n      <th>TEMPERATURE_DIFF</th>\n      <th>OXYGEN_SATURATION_DIFF</th>\n      <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n      <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n      <th>HEART_RATE_DIFF_REL</th>\n      <th>RESPIRATORY_RATE_DIFF_REL</th>\n      <th>TEMPERATURE_DIFF_REL</th>\n      <th>OXYGEN_SATURATION_DIFF_REL</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>DISEASE GROUPING 1</th>\n      <td>1.000000</td>\n      <td>0.232833</td>\n      <td>0.259372</td>\n      <td>0.018628</td>\n      <td>0.347914</td>\n      <td>0.199194</td>\n      <td>0.464991</td>\n      <td>0.187721</td>\n      <td>0.128779</td>\n      <td>0.013398</td>\n      <td>...</td>\n      <td>0.188455</td>\n      <td>0.042852</td>\n      <td>0.076589</td>\n      <td>0.049611</td>\n      <td>0.048554</td>\n      <td>0.052073</td>\n      <td>0.162467</td>\n      <td>0.019039</td>\n      <td>0.078430</td>\n      <td>0.049045</td>\n    </tr>\n    <tr>\n      <th>DISEASE GROUPING 2</th>\n      <td>0.232833</td>\n      <td>1.000000</td>\n      <td>0.072901</td>\n      <td>0.107559</td>\n      <td>0.152228</td>\n      <td>0.031235</td>\n      <td>0.140774</td>\n      <td>0.122963</td>\n      <td>0.069931</td>\n      <td>0.037642</td>\n      <td>...</td>\n      <td>0.227727</td>\n      <td>0.159572</td>\n      <td>0.219816</td>\n      <td>0.259062</td>\n      <td>0.073528</td>\n      <td>0.028948</td>\n      <td>0.224602</td>\n      <td>0.146439</td>\n      <td>0.221817</td>\n      <td>0.267425</td>\n    </tr>\n    <tr>\n      <th>DISEASE GROUPING 3</th>\n      <td>0.259372</td>\n      <td>0.072901</td>\n      <td>1.000000</td>\n      <td>0.300146</td>\n      <td>0.275185</td>\n      <td>0.268365</td>\n      <td>0.470774</td>\n      <td>0.067851</td>\n      <td>0.140691</td>\n      <td>0.009800</td>\n      <td>...</td>\n      <td>0.022354</td>\n      <td>0.031549</td>\n      <td>0.006847</td>\n      <td>0.012592</td>\n      <td>0.005903</td>\n      <td>0.017722</td>\n      <td>0.018194</td>\n      <td>0.026603</td>\n      <td>0.005393</td>\n      <td>0.011904</td>\n    </tr>\n    <tr>\n      <th>DISEASE GROUPING 4</th>\n      <td>0.018628</td>\n      <td>0.107559</td>\n      <td>0.300146</td>\n      <td>1.000000</td>\n      <td>0.127296</td>\n      <td>0.173474</td>\n      <td>0.129277</td>\n      <td>0.102198</td>\n      <td>0.061509</td>\n      <td>0.035429</td>\n      <td>...</td>\n      <td>0.022661</td>\n      <td>0.005775</td>\n      <td>0.013765</td>\n      <td>0.025052</td>\n      <td>0.043890</td>\n      <td>0.030982</td>\n      <td>0.030845</td>\n      <td>0.013118</td>\n      <td>0.014426</td>\n      <td>0.024363</td>\n    </tr>\n    <tr>\n      <th>DISEASE GROUPING 5</th>\n      <td>0.347914</td>\n      <td>0.152228</td>\n      <td>0.275185</td>\n      <td>0.127296</td>\n      <td>1.000000</td>\n      <td>0.129764</td>\n      <td>0.437339</td>\n      <td>0.101896</td>\n      <td>0.147387</td>\n      <td>0.046343</td>\n      <td>...</td>\n      <td>0.094562</td>\n      <td>0.021797</td>\n      <td>0.040025</td>\n      <td>0.012239</td>\n      <td>0.018934</td>\n      <td>0.057854</td>\n      <td>0.055391</td>\n      <td>0.006599</td>\n      <td>0.041944</td>\n      <td>0.012808</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n      <td>0.052073</td>\n      <td>0.028948</td>\n      <td>0.017722</td>\n      <td>0.030982</td>\n      <td>0.057854</td>\n      <td>0.001202</td>\n      <td>0.017899</td>\n      <td>0.042838</td>\n      <td>0.036087</td>\n      <td>0.062668</td>\n      <td>...</td>\n      <td>0.315267</td>\n      <td>0.339359</td>\n      <td>0.329350</td>\n      <td>0.432987</td>\n      <td>0.776732</td>\n      <td>1.000000</td>\n      <td>0.349502</td>\n      <td>0.386949</td>\n      <td>0.323661</td>\n      <td>0.429762</td>\n    </tr>\n    <tr>\n      <th>HEART_RATE_DIFF_REL</th>\n      <td>0.162467</td>\n      <td>0.224602</td>\n      <td>0.018194</td>\n      <td>0.030845</td>\n      <td>0.055391</td>\n      <td>0.004998</td>\n      <td>0.110824</td>\n      <td>0.024410</td>\n      <td>0.049761</td>\n      <td>0.030320</td>\n      <td>...</td>\n      <td>0.972805</td>\n      <td>0.558114</td>\n      <td>0.686505</td>\n      <td>0.629669</td>\n      <td>0.321500</td>\n      <td>0.349502</td>\n      <td>1.000000</td>\n      <td>0.555210</td>\n      <td>0.689311</td>\n      <td>0.623838</td>\n    </tr>\n    <tr>\n      <th>RESPIRATORY_RATE_DIFF_REL</th>\n      <td>0.019039</td>\n      <td>0.146439</td>\n      <td>0.026603</td>\n      <td>0.013118</td>\n      <td>0.006599</td>\n      <td>0.048619</td>\n      <td>0.010122</td>\n      <td>0.006205</td>\n      <td>0.033245</td>\n      <td>0.008773</td>\n      <td>...</td>\n      <td>0.523308</td>\n      <td>0.973030</td>\n      <td>0.539410</td>\n      <td>0.517766</td>\n      <td>0.365927</td>\n      <td>0.386949</td>\n      <td>0.555210</td>\n      <td>1.000000</td>\n      <td>0.537379</td>\n      <td>0.513188</td>\n    </tr>\n    <tr>\n      <th>TEMPERATURE_DIFF_REL</th>\n      <td>0.078430</td>\n      <td>0.221817</td>\n      <td>0.005393</td>\n      <td>0.014426</td>\n      <td>0.041944</td>\n      <td>0.006971</td>\n      <td>0.085658</td>\n      <td>0.019229</td>\n      <td>0.006927</td>\n      <td>0.021769</td>\n      <td>...</td>\n      <td>0.645859</td>\n      <td>0.539246</td>\n      <td>0.999628</td>\n      <td>0.706897</td>\n      <td>0.289099</td>\n      <td>0.323661</td>\n      <td>0.689311</td>\n      <td>0.537379</td>\n      <td>1.000000</td>\n      <td>0.701367</td>\n    </tr>\n    <tr>\n      <th>OXYGEN_SATURATION_DIFF_REL</th>\n      <td>0.049045</td>\n      <td>0.267425</td>\n      <td>0.011904</td>\n      <td>0.024363</td>\n      <td>0.012808</td>\n      <td>0.033638</td>\n      <td>0.057847</td>\n      <td>0.074031</td>\n      <td>0.014101</td>\n      <td>0.063720</td>\n      <td>...</td>\n      <td>0.589067</td>\n      <td>0.522798</td>\n      <td>0.701965</td>\n      <td>0.999488</td>\n      <td>0.405593</td>\n      <td>0.429762</td>\n      <td>0.623838</td>\n      <td>0.513188</td>\n      <td>0.701367</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>225 rows × 225 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"threshold = 0.95\ncolunas_corr = [col for col in matriz_corr.columns if any(matriz_corr[col] > threshold)]\ncolunas_corr","execution_count":91,"outputs":[{"output_type":"execute_result","execution_count":91,"data":{"text/plain":"['DISEASE GROUPING 1',\n 'DISEASE GROUPING 2',\n 'DISEASE GROUPING 3',\n 'DISEASE GROUPING 4',\n 'DISEASE GROUPING 5',\n 'DISEASE GROUPING 6',\n 'HTN',\n 'IMMUNOCOMPROMISED',\n 'OTHER',\n 'ALBUMIN_MEDIAN',\n 'ALBUMIN_MEAN',\n 'ALBUMIN_MIN',\n 'ALBUMIN_MAX',\n 'BE_ARTERIAL_MEDIAN',\n 'BE_ARTERIAL_MEAN',\n 'BE_ARTERIAL_MIN',\n 'BE_ARTERIAL_MAX',\n 'BE_VENOUS_MEDIAN',\n 'BE_VENOUS_MEAN',\n 'BE_VENOUS_MIN',\n 'BE_VENOUS_MAX',\n 'BIC_ARTERIAL_MEDIAN',\n 'BIC_ARTERIAL_MEAN',\n 'BIC_ARTERIAL_MIN',\n 'BIC_ARTERIAL_MAX',\n 'BIC_VENOUS_MEDIAN',\n 'BIC_VENOUS_MEAN',\n 'BIC_VENOUS_MIN',\n 'BIC_VENOUS_MAX',\n 'BILLIRUBIN_MEDIAN',\n 'BILLIRUBIN_MEAN',\n 'BILLIRUBIN_MIN',\n 'BILLIRUBIN_MAX',\n 'BLAST_MEDIAN',\n 'BLAST_MEAN',\n 'BLAST_MIN',\n 'BLAST_MAX',\n 'CALCIUM_MEDIAN',\n 'CALCIUM_MEAN',\n 'CALCIUM_MIN',\n 'CALCIUM_MAX',\n 'CREATININ_MEDIAN',\n 'CREATININ_MEAN',\n 'CREATININ_MIN',\n 'CREATININ_MAX',\n 'FFA_MEDIAN',\n 'FFA_MEAN',\n 'FFA_MIN',\n 'FFA_MAX',\n 'GGT_MEDIAN',\n 'GGT_MEAN',\n 'GGT_MIN',\n 'GGT_MAX',\n 'GLUCOSE_MEDIAN',\n 'GLUCOSE_MEAN',\n 'GLUCOSE_MIN',\n 'GLUCOSE_MAX',\n 'HEMATOCRITE_MEDIAN',\n 'HEMATOCRITE_MEAN',\n 'HEMATOCRITE_MIN',\n 'HEMATOCRITE_MAX',\n 'HEMOGLOBIN_MEDIAN',\n 'HEMOGLOBIN_MEAN',\n 'HEMOGLOBIN_MIN',\n 'HEMOGLOBIN_MAX',\n 'INR_MEDIAN',\n 'INR_MEAN',\n 'INR_MIN',\n 'INR_MAX',\n 'LACTATE_MEDIAN',\n 'LACTATE_MEAN',\n 'LACTATE_MIN',\n 'LACTATE_MAX',\n 'LEUKOCYTES_MEDIAN',\n 'LEUKOCYTES_MEAN',\n 'LEUKOCYTES_MIN',\n 'LEUKOCYTES_MAX',\n 'LINFOCITOS_MEDIAN',\n 'LINFOCITOS_MEAN',\n 'LINFOCITOS_MIN',\n 'LINFOCITOS_MAX',\n 'NEUTROPHILES_MEDIAN',\n 'NEUTROPHILES_MEAN',\n 'NEUTROPHILES_MIN',\n 'NEUTROPHILES_MAX',\n 'P02_ARTERIAL_MEDIAN',\n 'P02_ARTERIAL_MEAN',\n 'P02_ARTERIAL_MIN',\n 'P02_ARTERIAL_MAX',\n 'P02_VENOUS_MEDIAN',\n 'P02_VENOUS_MEAN',\n 'P02_VENOUS_MIN',\n 'P02_VENOUS_MAX',\n 'PC02_ARTERIAL_MEDIAN',\n 'PC02_ARTERIAL_MEAN',\n 'PC02_ARTERIAL_MIN',\n 'PC02_ARTERIAL_MAX',\n 'PC02_VENOUS_MEDIAN',\n 'PC02_VENOUS_MEAN',\n 'PC02_VENOUS_MIN',\n 'PC02_VENOUS_MAX',\n 'PCR_MEDIAN',\n 'PCR_MEAN',\n 'PCR_MIN',\n 'PCR_MAX',\n 'PH_ARTERIAL_MEDIAN',\n 'PH_ARTERIAL_MEAN',\n 'PH_ARTERIAL_MIN',\n 'PH_ARTERIAL_MAX',\n 'PH_VENOUS_MEDIAN',\n 'PH_VENOUS_MEAN',\n 'PH_VENOUS_MIN',\n 'PH_VENOUS_MAX',\n 'PLATELETS_MEDIAN',\n 'PLATELETS_MEAN',\n 'PLATELETS_MIN',\n 'PLATELETS_MAX',\n 'POTASSIUM_MEDIAN',\n 'POTASSIUM_MEAN',\n 'POTASSIUM_MIN',\n 'POTASSIUM_MAX',\n 'SAT02_ARTERIAL_MEDIAN',\n 'SAT02_ARTERIAL_MEAN',\n 'SAT02_ARTERIAL_MIN',\n 'SAT02_ARTERIAL_MAX',\n 'SAT02_VENOUS_MEDIAN',\n 'SAT02_VENOUS_MEAN',\n 'SAT02_VENOUS_MIN',\n 'SAT02_VENOUS_MAX',\n 'SODIUM_MEDIAN',\n 'SODIUM_MEAN',\n 'SODIUM_MIN',\n 'SODIUM_MAX',\n 'TGO_MEDIAN',\n 'TGO_MEAN',\n 'TGO_MIN',\n 'TGO_MAX',\n 'TGP_MEDIAN',\n 'TGP_MEAN',\n 'TGP_MIN',\n 'TGP_MAX',\n 'TTPA_MEDIAN',\n 'TTPA_MEAN',\n 'TTPA_MIN',\n 'TTPA_MAX',\n 'UREA_MEDIAN',\n 'UREA_MEAN',\n 'UREA_MIN',\n 'UREA_MAX',\n 'DIMER_MEDIAN',\n 'DIMER_MEAN',\n 'DIMER_MIN',\n 'DIMER_MAX',\n 'BLOODPRESSURE_DIASTOLIC_MEAN',\n 'BLOODPRESSURE_SISTOLIC_MEAN',\n 'HEART_RATE_MEAN',\n 'RESPIRATORY_RATE_MEAN',\n 'TEMPERATURE_MEAN',\n 'OXYGEN_SATURATION_MEAN',\n 'BLOODPRESSURE_DIASTOLIC_MEDIAN',\n 'BLOODPRESSURE_SISTOLIC_MEDIAN',\n 'HEART_RATE_MEDIAN',\n 'RESPIRATORY_RATE_MEDIAN',\n 'TEMPERATURE_MEDIAN',\n 'OXYGEN_SATURATION_MEDIAN',\n 'BLOODPRESSURE_DIASTOLIC_MIN',\n 'BLOODPRESSURE_SISTOLIC_MIN',\n 'HEART_RATE_MIN',\n 'RESPIRATORY_RATE_MIN',\n 'TEMPERATURE_MIN',\n 'OXYGEN_SATURATION_MIN',\n 'BLOODPRESSURE_DIASTOLIC_MAX',\n 'BLOODPRESSURE_SISTOLIC_MAX',\n 'HEART_RATE_MAX',\n 'RESPIRATORY_RATE_MAX',\n 'TEMPERATURE_MAX',\n 'OXYGEN_SATURATION_MAX',\n 'BLOODPRESSURE_DIASTOLIC_DIFF',\n 'BLOODPRESSURE_SISTOLIC_DIFF',\n 'HEART_RATE_DIFF',\n 'RESPIRATORY_RATE_DIFF',\n 'TEMPERATURE_DIFF',\n 'OXYGEN_SATURATION_DIFF',\n 'BLOODPRESSURE_DIASTOLIC_DIFF_REL',\n 'BLOODPRESSURE_SISTOLIC_DIFF_REL',\n 'HEART_RATE_DIFF_REL',\n 'RESPIRATORY_RATE_DIFF_REL',\n 'TEMPERATURE_DIFF_REL',\n 'OXYGEN_SATURATION_DIFF_REL']"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Utilizando uma função simples como essa retornou MUITAS features. Isso ocorreu por 2 motivos:\n* Toda feature tem correlação 1 com ela mesma na diagonal principal.\n* A parte abaixo da diagonal principal na matriz é um espelho da parte de cima. Nossa função excluiria ambas as colunas correlacionadas, ao invés de apenas uma.\n\nPara resolver isso, podemos utilizar o numpy e suas funções de álgebra linear."},{"metadata":{"trusted":true},"cell_type":"code","source":"matriz_superior = matriz_corr.where(np.triu(np.ones(matriz_corr.shape), k = 1).astype(np.bool))","execution_count":93,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"colunas_corr = [col for col in matriz_superior.columns if any(matriz_superior[col] > threshold)]\ncolunas_corr","execution_count":94,"outputs":[{"output_type":"execute_result","execution_count":94,"data":{"text/plain":"['ALBUMIN_MEAN',\n 'ALBUMIN_MIN',\n 'ALBUMIN_MAX',\n 'BE_ARTERIAL_MEAN',\n 'BE_ARTERIAL_MIN',\n 'BE_ARTERIAL_MAX',\n 'BE_VENOUS_MEAN',\n 'BE_VENOUS_MIN',\n 'BE_VENOUS_MAX',\n 'BIC_ARTERIAL_MEAN',\n 'BIC_ARTERIAL_MIN',\n 'BIC_ARTERIAL_MAX',\n 'BIC_VENOUS_MEAN',\n 'BIC_VENOUS_MIN',\n 'BIC_VENOUS_MAX',\n 'BILLIRUBIN_MEAN',\n 'BILLIRUBIN_MIN',\n 'BILLIRUBIN_MAX',\n 'BLAST_MEAN',\n 'BLAST_MIN',\n 'BLAST_MAX',\n 'CALCIUM_MEAN',\n 'CALCIUM_MIN',\n 'CALCIUM_MAX',\n 'CREATININ_MEAN',\n 'CREATININ_MIN',\n 'CREATININ_MAX',\n 'FFA_MEAN',\n 'FFA_MIN',\n 'FFA_MAX',\n 'GGT_MEAN',\n 'GGT_MIN',\n 'GGT_MAX',\n 'GLUCOSE_MEAN',\n 'GLUCOSE_MIN',\n 'GLUCOSE_MAX',\n 'HEMATOCRITE_MEAN',\n 'HEMATOCRITE_MIN',\n 'HEMATOCRITE_MAX',\n 'HEMOGLOBIN_MEDIAN',\n 'HEMOGLOBIN_MEAN',\n 'HEMOGLOBIN_MIN',\n 'HEMOGLOBIN_MAX',\n 'INR_MEAN',\n 'INR_MIN',\n 'INR_MAX',\n 'LACTATE_MEAN',\n 'LACTATE_MIN',\n 'LACTATE_MAX',\n 'LEUKOCYTES_MEAN',\n 'LEUKOCYTES_MIN',\n 'LEUKOCYTES_MAX',\n 'LINFOCITOS_MEAN',\n 'LINFOCITOS_MIN',\n 'LINFOCITOS_MAX',\n 'NEUTROPHILES_MEDIAN',\n 'NEUTROPHILES_MEAN',\n 'NEUTROPHILES_MIN',\n 'NEUTROPHILES_MAX',\n 'P02_ARTERIAL_MEAN',\n 'P02_ARTERIAL_MIN',\n 'P02_ARTERIAL_MAX',\n 'P02_VENOUS_MEAN',\n 'P02_VENOUS_MIN',\n 'P02_VENOUS_MAX',\n 'PC02_ARTERIAL_MEAN',\n 'PC02_ARTERIAL_MIN',\n 'PC02_ARTERIAL_MAX',\n 'PC02_VENOUS_MEAN',\n 'PC02_VENOUS_MIN',\n 'PC02_VENOUS_MAX',\n 'PCR_MEAN',\n 'PCR_MIN',\n 'PCR_MAX',\n 'PH_ARTERIAL_MEAN',\n 'PH_ARTERIAL_MIN',\n 'PH_ARTERIAL_MAX',\n 'PH_VENOUS_MEAN',\n 'PH_VENOUS_MIN',\n 'PH_VENOUS_MAX',\n 'PLATELETS_MEAN',\n 'PLATELETS_MIN',\n 'PLATELETS_MAX',\n 'POTASSIUM_MEAN',\n 'POTASSIUM_MIN',\n 'POTASSIUM_MAX',\n 'SAT02_ARTERIAL_MEAN',\n 'SAT02_ARTERIAL_MIN',\n 'SAT02_ARTERIAL_MAX',\n 'SAT02_VENOUS_MEAN',\n 'SAT02_VENOUS_MIN',\n 'SAT02_VENOUS_MAX',\n 'SODIUM_MEAN',\n 'SODIUM_MIN',\n 'SODIUM_MAX',\n 'TGO_MEAN',\n 'TGO_MIN',\n 'TGO_MAX',\n 'TGP_MEAN',\n 'TGP_MIN',\n 'TGP_MAX',\n 'TTPA_MEAN',\n 'TTPA_MIN',\n 'TTPA_MAX',\n 'UREA_MEAN',\n 'UREA_MIN',\n 'UREA_MAX',\n 'DIMER_MEAN',\n 'DIMER_MIN',\n 'DIMER_MAX',\n 'BLOODPRESSURE_DIASTOLIC_MEDIAN',\n 'BLOODPRESSURE_SISTOLIC_MEDIAN',\n 'HEART_RATE_MEDIAN',\n 'RESPIRATORY_RATE_MEDIAN',\n 'TEMPERATURE_MEDIAN',\n 'OXYGEN_SATURATION_MEDIAN',\n 'BLOODPRESSURE_DIASTOLIC_MIN',\n 'BLOODPRESSURE_SISTOLIC_MIN',\n 'HEART_RATE_MIN',\n 'BLOODPRESSURE_SISTOLIC_MAX',\n 'HEART_RATE_MAX',\n 'RESPIRATORY_RATE_MAX',\n 'TEMPERATURE_MAX',\n 'OXYGEN_SATURATION_MAX',\n 'BLOODPRESSURE_DIASTOLIC_DIFF_REL',\n 'BLOODPRESSURE_SISTOLIC_DIFF_REL',\n 'HEART_RATE_DIFF_REL',\n 'RESPIRATORY_RATE_DIFF_REL',\n 'TEMPERATURE_DIFF_REL',\n 'OXYGEN_SATURATION_DIFF_REL']"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Criando uma função apenas para isso\ndef remove_var_correlacionadas(dados, valor_corte):\n    matriz_corr = dados.corr()\n    matriz_superior = matriz_corr.where(np.triu(np.ones(matriz_corr.shape), k = 1).astype(np.bool))\n    colunas_corr = [col for col in matriz_superior.columns if any(matriz_superior[col] > valor_corte)]\n    \n    return dados.drop(colunas_corr, axis = 1)\n\ndf_limpo_sem_corr = remove_var_correlacionadas(df_limpo, 0.95)","execution_count":97,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_limpo_sem_corr","execution_count":98,"outputs":[{"output_type":"execute_result","execution_count":98,"data":{"text/plain":"     PATIENT_VISIT_IDENTIFIER  AGE_ABOVE65  AGE_PERCENTIL  GENDER  \\\n0                           0            1              5       0   \n1                           1            1              8       1   \n2                           2            0              0       0   \n3                           3            0              3       1   \n4                           4            0              0       0   \n..                        ...          ...            ...     ...   \n379                       380            0              3       1   \n380                       381            1              9       0   \n381                       382            0              4       0   \n382                       383            0              3       1   \n383                       384            0              4       1   \n\n     DISEASE GROUPING 1  DISEASE GROUPING 2  DISEASE GROUPING 3  \\\n0                   0.0                 0.0                 0.0   \n1                   0.0                 0.0                 0.0   \n2                   0.0                 0.0                 0.0   \n3                   0.0                 0.0                 0.0   \n4                   0.0                 0.0                 0.0   \n..                  ...                 ...                 ...   \n379                 0.0                 0.0                 0.0   \n380                 0.0                 0.0                 0.0   \n381                 0.0                 0.0                 0.0   \n382                 0.0                 0.0                 0.0   \n383                 0.0                 0.0                 0.0   \n\n     DISEASE GROUPING 4  DISEASE GROUPING 5  DISEASE GROUPING 6  ...  \\\n0                   0.0                 1.0                 1.0  ...   \n1                   0.0                 0.0                 0.0  ...   \n2                   0.0                 0.0                 0.0  ...   \n3                   0.0                 0.0                 0.0  ...   \n4                   0.0                 0.0                 0.0  ...   \n..                  ...                 ...                 ...  ...   \n379                 0.0                 0.0                 0.0  ...   \n380                 0.0                 0.0                 0.0  ...   \n381                 0.0                 0.0                 0.0  ...   \n382                 0.0                 0.0                 0.0  ...   \n383                 0.0                 0.0                 0.0  ...   \n\n     OXYGEN_SATURATION_MIN  BLOODPRESSURE_DIASTOLIC_MAX  \\\n0                 0.898990                    -0.247863   \n1                 1.000000                    -0.504274   \n2                 0.959596                    -0.435897   \n3                 0.878788                    -0.299145   \n4                 0.939394                    -0.076923   \n..                     ...                          ...   \n379               0.898990                    -0.418803   \n380               0.818182                    -0.589744   \n381               0.959596                    -0.299145   \n382               0.898990                    -0.247863   \n383               0.919192                    -0.299145   \n\n     BLOODPRESSURE_DIASTOLIC_DIFF  BLOODPRESSURE_SISTOLIC_DIFF  \\\n0                       -1.000000                    -1.000000   \n1                       -1.000000                    -1.000000   \n2                       -0.547826                    -0.533742   \n3                       -1.000000                    -1.000000   \n4                       -1.000000                    -0.877301   \n..                            ...                          ...   \n379                     -1.000000                    -1.000000   \n380                     -1.000000                    -1.000000   \n381                     -1.000000                    -1.000000   \n382                     -1.000000                    -1.000000   \n383                     -1.000000                    -1.000000   \n\n     HEART_RATE_DIFF  RESPIRATORY_RATE_DIFF  TEMPERATURE_DIFF  \\\n0          -1.000000              -1.000000         -1.000000   \n1          -1.000000              -1.000000         -1.000000   \n2          -0.603053              -0.764706         -1.000000   \n3          -1.000000              -1.000000         -1.000000   \n4          -0.923664              -0.882353         -0.952381   \n..               ...                    ...               ...   \n379        -1.000000              -1.000000         -1.000000   \n380        -1.000000              -1.000000         -0.619048   \n381        -1.000000              -1.000000         -1.000000   \n382        -1.000000              -1.000000         -1.000000   \n383        -1.000000              -1.000000         -1.000000   \n\n     OXYGEN_SATURATION_DIFF  WINDOW  ICU  \n0                 -1.000000     0-2    1  \n1                 -1.000000     0-2    1  \n2                 -0.959596     0-2    1  \n3                 -1.000000     0-2    0  \n4                 -0.979798     0-2    0  \n..                      ...     ...  ...  \n379               -1.000000     0-2    1  \n380               -1.000000     0-2    0  \n381               -1.000000     0-2    1  \n382               -1.000000     0-2    0  \n383               -1.000000     0-2    0  \n\n[384 rows x 101 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PATIENT_VISIT_IDENTIFIER</th>\n      <th>AGE_ABOVE65</th>\n      <th>AGE_PERCENTIL</th>\n      <th>GENDER</th>\n      <th>DISEASE GROUPING 1</th>\n      <th>DISEASE GROUPING 2</th>\n      <th>DISEASE GROUPING 3</th>\n      <th>DISEASE GROUPING 4</th>\n      <th>DISEASE GROUPING 5</th>\n      <th>DISEASE GROUPING 6</th>\n      <th>...</th>\n      <th>OXYGEN_SATURATION_MIN</th>\n      <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n      <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n      <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n      <th>HEART_RATE_DIFF</th>\n      <th>RESPIRATORY_RATE_DIFF</th>\n      <th>TEMPERATURE_DIFF</th>\n      <th>OXYGEN_SATURATION_DIFF</th>\n      <th>WINDOW</th>\n      <th>ICU</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>0.898990</td>\n      <td>-0.247863</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>8</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>-0.504274</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.959596</td>\n      <td>-0.435897</td>\n      <td>-0.547826</td>\n      <td>-0.533742</td>\n      <td>-0.603053</td>\n      <td>-0.764706</td>\n      <td>-1.000000</td>\n      <td>-0.959596</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.878788</td>\n      <td>-0.299145</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.939394</td>\n      <td>-0.076923</td>\n      <td>-1.000000</td>\n      <td>-0.877301</td>\n      <td>-0.923664</td>\n      <td>-0.882353</td>\n      <td>-0.952381</td>\n      <td>-0.979798</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>379</th>\n      <td>380</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.898990</td>\n      <td>-0.418803</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>380</th>\n      <td>381</td>\n      <td>1</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.818182</td>\n      <td>-0.589744</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-0.619048</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>381</th>\n      <td>382</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.959596</td>\n      <td>-0.299145</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>382</th>\n      <td>383</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.898990</td>\n      <td>-0.247863</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>383</th>\n      <td>384</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.919192</td>\n      <td>-0.299145</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>0-2</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>384 rows × 101 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"lr_model = LogisticRegression(max_iter = 2000, C= 0.1)\n\nroda_modelo_cv(lr_model, df_limpo, 5, 10)","execution_count":109,"outputs":[{"output_type":"stream","text":"AUC Médio: 0.8020943210416895\nAUC STD: 0.05465530976215416\nLOW AUC: 0.6927837015173812 - HIGH AUC: 0.9114049405659979\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"roda_modelo_cv(lr_model, df_limpo_sem_corr, 5, 10)","execution_count":110,"outputs":[{"output_type":"stream","text":"AUC Médio: 0.7882897472371159\nAUC STD: 0.0544458673431708\nLOW AUC: 0.6793980125507743 - HIGH AUC: 0.8971814819234575\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"#### Desafio 14: Ler no sklearn sobre feature selection, feature importance\n#### Desafio 15: Repassar pelo desafio 12, com as bases de dados alteradas.\n\nResolvi esses desafios utilizando o projeto final, que usa a mesma base de dados!"}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"}},"nbformat":4,"nbformat_minor":4}