bAVM-Modeling (Logistic Regression LR).ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPvzSQ/X0opg3jS7NyZO+Qz"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Machine Learning approach to analyze 3-Year prognostication for patients with brain arteriovenous malformation (bAVM) after stereotactic radiosurgery (SRS): a study for a small and heterogeneous group in Peru.\n","\n","> [Python - sklearn]\n","---"],"metadata":{"id":"ox3kIF0HDaXH"}},{"cell_type":"markdown","source":["## Setup Environment"],"metadata":{"id":"tNtrnU09D3Qa"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ypRkVpt_7_6J","executionInfo":{"status":"ok","timestamp":1671590283102,"user_tz":300,"elapsed":7381,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"outputId":"07874a50-16b0-4033-e000-97417e5765a6"},"outputs":[{"output_type":"stream","name":"stdout","text":["Scikit-learn version:  1.0.2\n","Python version:  3.8.16\n","Mounted at /content/drive\n"]}],"source":["# sklearn\n","import sklearn\n","print('Scikit-learn version: ', sklearn.__version__)\n","\n","# Python\n","import platform\n","print('Python version: ', platform.python_version())\n","\n","# Mount drive\n","from google.colab import drive\n","drive.mount('/content/drive', force_remount=True)"]},{"cell_type":"code","source":["%%bash \n","MYPROJECT=\"/content/drive/MyDrive/UNI/DSc/Publications/Mirko/Paper_Propuesta/Propuesta01/Modeling\"\n","ls -l \"$MYPROJECT\"\n","echo \"$MYPROJECT\" > config.txt"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RaRwGyE5Dng6","executionInfo":{"status":"ok","timestamp":1671590283103,"user_tz":300,"elapsed":11,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"outputId":"ba09dbce-6046-481c-b119-695d2a7e1c8c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["total 2492\n","-rw------- 1 root root  670793 Dec 13 02:43 bAVM-Dataset Pre-Processing\n","-rw------- 1 root root 1096156 Dec 21 02:30 bAVM-Modeling (Decision Trees DT)\n","-rw------- 1 root root  410913 Dec 21 02:29 bAVM-Modeling (Logistic Regression LR)\n","-rw------- 1 root root  344685 Dec 21 00:35 bAVM-Modeling (Logistic Regression LR).bkp\n","drwx------ 2 root root    4096 Nov 30 15:23 bkp\n","drwx------ 2 root root    4096 Nov  2 22:23 dataset\n","drwx------ 2 root root    4096 Nov  3 00:41 model\n","drwx------ 2 root root    4096 Nov  3 01:51 output\n","-rw------- 1 root root    7429 Dec 20 22:28 Util.ipynb\n","drwx------ 2 root root    4096 Nov  3 00:41 utils\n"]}]},{"cell_type":"markdown","source":["### Libraries"],"metadata":{"id":"jKQtfGdoAnik"}},{"cell_type":"code","source":["!pip install import-ipynb"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Syeaut5uHuR7","executionInfo":{"status":"ok","timestamp":1671590289129,"user_tz":300,"elapsed":6031,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"outputId":"61f52a55-3cd9-4ff0-8725-575c8a9615dc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: import-ipynb in /usr/local/lib/python3.8/dist-packages (0.1.4)\n","Requirement already satisfied: nbformat in /usr/local/lib/python3.8/dist-packages (from import-ipynb) (5.7.0)\n","Requirement already satisfied: IPython in /usr/local/lib/python3.8/dist-packages (from import-ipynb) (7.9.0)\n","Requirement already satisfied: decorator in /usr/local/lib/python3.8/dist-packages (from IPython->import-ipynb) (4.4.2)\n","Requirement already satisfied: pickleshare in /usr/local/lib/python3.8/dist-packages (from IPython->import-ipynb) (0.7.5)\n","Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from IPython->import-ipynb) (2.0.10)\n","Requirement already satisfied: pygments in /usr/local/lib/python3.8/dist-packages (from IPython->import-ipynb) (2.6.1)\n","Requirement already satisfied: backcall in /usr/local/lib/python3.8/dist-packages (from IPython->import-ipynb) (0.2.0)\n","Requirement already satisfied: pexpect in /usr/local/lib/python3.8/dist-packages (from IPython->import-ipynb) (4.8.0)\n","Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.8/dist-packages (from IPython->import-ipynb) (5.7.1)\n","Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.8/dist-packages (from IPython->import-ipynb) (57.4.0)\n","Requirement already satisfied: jedi>=0.10 in /usr/local/lib/python3.8/dist-packages (from IPython->import-ipynb) (0.18.2)\n","Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.8/dist-packages (from jedi>=0.10->IPython->import-ipynb) (0.8.3)\n","Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.8/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->IPython->import-ipynb) (1.15.0)\n","Requirement already satisfied: wcwidth in /usr/local/lib/python3.8/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->IPython->import-ipynb) (0.2.5)\n","Requirement already satisfied: jupyter-core in /usr/local/lib/python3.8/dist-packages (from nbformat->import-ipynb) (5.1.0)\n","Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.8/dist-packages (from nbformat->import-ipynb) (4.3.3)\n","Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.8/dist-packages (from nbformat->import-ipynb) (2.16.2)\n","Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat->import-ipynb) (0.19.2)\n","Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat->import-ipynb) (22.1.0)\n","Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat->import-ipynb) (5.10.1)\n","Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.8/dist-packages (from importlib-resources>=1.4.0->jsonschema>=2.6->nbformat->import-ipynb) (3.11.0)\n","Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.8/dist-packages (from jupyter-core->nbformat->import-ipynb) (2.6.0)\n","Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.8/dist-packages (from pexpect->IPython->import-ipynb) (0.7.0)\n"]}]},{"cell_type":"code","source":["# Libs\n","import math\n","import import_ipynb\n","\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import joblib\n","\n","from datetime import datetime\n","\n","from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import ConfusionMatrixDisplay\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import roc_auc_score\n","from sklearn.metrics import roc_curve\n","\n","from sklearn.preprocessing import MinMaxScaler\n","\n","# Function\n","from sklearn.linear_model import LogisticRegression"],"metadata":{"id":"dFRsS5DSAiAf"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### General definitions:"],"metadata":{"id":"Ik0EKog7X4ib"}},{"cell_type":"code","source":["# Project & working folders\n","PROJECT_FOLDER = open('config.txt').readline().rstrip('\\n')\n","MODEL_FOLDER = \"/\".join([PROJECT_FOLDER,\"model\"])\n","OUTPUT_FOLDER = \"/\".join([PROJECT_FOLDER,\"output\"])\n","\n","%cd $PROJECT_FOLDER\n","%ls -l\n","from Util import trainAndGetBestModel, crossValidation, plot_roc_curve, getMetrics\n","\n","# Model name\n","MODEL_NAME = 'Logistic-Regression'\n","HYPER_PARAMS = {'penalty': ['l1', 'l2'],\n","                'solver': ['liblinear'],\n","                'C': [0.001,0.01,0.1,1,10,100,1000],\n","                'max_iter':[1000, 5000]}\n","\n","# Tunning model\n","N_FOLDS = 8 # VAL_SIZE = (100%/N_FOLDS)\n","\n","# Target\n","CLASS = 'curation'\n","\n","# Guarantee the same result\n","RANDOM_SEED = 0\n","\n","# Hyper parameter tunning\n","\n","METRIC='balanced_accuracy'\n","\n","# Report\n","FINAL_REPORT_METRICS = []\n","FINAL_REPORT_METRICS_TITLE = ['Model Name','Info','Accuracy', 'Sensitivity/Recall', 'Specificity', 'PPV', 'NPV','Balanced_accuracy','F1', 'AUC', 'TN', 'FP', 'FN', 'TP']\n","\n","# Others\n","DPI_DEFAULT = 400\n","DT_STRING = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n","\n","print('Date time: ' + DT_STRING)"],"metadata":{"id":"A3SrGv1qG3VW","executionInfo":{"status":"ok","timestamp":1671590290555,"user_tz":300,"elapsed":93,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"38231dea-24b8-4649-e127-5eb4210f5ba5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/UNI/DSc/Publications/Mirko/Paper_Propuesta/Propuesta01/Modeling\n","total 2492\n","-rw------- 1 root root  670793 Dec 13 02:43 'bAVM-Dataset Pre-Processing'\n","-rw------- 1 root root 1096156 Dec 21 02:30 'bAVM-Modeling (Decision Trees DT)'\n","-rw------- 1 root root  410913 Dec 21 02:29 'bAVM-Modeling (Logistic Regression LR)'\n","-rw------- 1 root root  344685 Dec 21 00:35 'bAVM-Modeling (Logistic Regression LR).bkp'\n","drwx------ 2 root root    4096 Nov 30 15:23  \u001b[0m\u001b[01;34mbkp\u001b[0m/\n","drwx------ 2 root root    4096 Nov  2 22:23  \u001b[01;34mdataset\u001b[0m/\n","drwx------ 3 root root    4096 Nov  3 00:41  \u001b[01;34mmodel\u001b[0m/\n","drwx------ 2 root root    4096 Nov  3 01:51  \u001b[01;34moutput\u001b[0m/\n","-rw------- 1 root root    7429 Dec 20 22:28  Util.ipynb\n","drwx------ 2 root root    4096 Nov  3 00:41  \u001b[01;34mutils\u001b[0m/\n","importing Jupyter notebook from Util.ipynb\n","Date time: 20221221023812\n"]}]},{"cell_type":"markdown","source":["## **Part I: Loading Datasets:**"],"metadata":{"id":"UO-IVm9sGSs4"}},{"cell_type":"code","source":["# dataset\n","dataset_name_csv = 'training_set.csv'\n","dataset_full = pd.read_csv(\"/\".join([OUTPUT_FOLDER, dataset_name_csv]))\n","\n","# balanced dataset\n","bal_dataset_name_csv = 'training_set_bal.csv'\n","bal_dataset_full = pd.read_csv(\"/\".join([OUTPUT_FOLDER, bal_dataset_name_csv]))\n","bal_dataset_full.head()"],"metadata":{"id":"u1hX33on6xDt","executionInfo":{"status":"ok","timestamp":1671590290555,"user_tz":300,"elapsed":80,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"colab":{"base_uri":"https://localhost:8080/","height":300},"outputId":"780d9b37-5f3b-4cb8-c247-ff1d90ce576d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   gender   age  ocupation  haemorrhage  headache  seizures  encephalomalacia  \\\n","0     0.0  43.0        3.0          1.0       1.0       0.0               0.0   \n","1     1.0  25.0        2.0          1.0       1.0       1.0               0.0   \n","2     1.0  21.0        2.0          1.0       1.0       1.0               1.0   \n","3     1.0  29.0        4.0          1.0       1.0       0.0               1.0   \n","4     0.0  24.0        3.0          1.0       0.0       0.0               1.0   \n","\n","   other_diseases  deficit  karnofsky_scale  ...  type_venous_drainage  \\\n","0             0.0      1.0             60.0  ...                   2.0   \n","1             0.0      1.0             90.0  ...                   1.0   \n","2             0.0      1.0             90.0  ...                   3.0   \n","3             0.0      2.0             90.0  ...                   1.0   \n","4             0.0      4.0             90.0  ...                   2.0   \n","\n","   eloquence  blood_flow_velocity  venous_stenosis  volumen_avm  \\\n","0        1.0                  1.0              0.0        1.372   \n","1        1.0                  3.0              0.0        7.670   \n","2        1.0                  2.0              1.0        2.040   \n","3        0.0                  1.0              0.0        0.256   \n","4        0.0                  1.0              0.0        0.500   \n","\n","   num_radiosurgeries  num_isocenters  radiation_dosis  isodosis  curation  \n","0                 1.0             1.0             17.0      80.0       1.0  \n","1                 1.0             2.0             13.0      50.0       1.0  \n","2                 1.0             1.0             18.0      80.0       1.0  \n","3                 1.0             1.0             17.0      80.0       1.0  \n","4                 1.0             1.0             18.0      80.0       1.0  \n","\n","[5 rows x 33 columns]"],"text/html":["\n","  <div id=\"df-85fc8b87-de0d-4d85-bb76-0f3c3a5f582b\">\n","    <div class=\"colab-df-container\">\n","      <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>gender</th>\n","      <th>age</th>\n","      <th>ocupation</th>\n","      <th>haemorrhage</th>\n","      <th>headache</th>\n","      <th>seizures</th>\n","      <th>encephalomalacia</th>\n","      <th>other_diseases</th>\n","      <th>deficit</th>\n","      <th>karnofsky_scale</th>\n","      <th>...</th>\n","      <th>type_venous_drainage</th>\n","      <th>eloquence</th>\n","      <th>blood_flow_velocity</th>\n","      <th>venous_stenosis</th>\n","      <th>volumen_avm</th>\n","      <th>num_radiosurgeries</th>\n","      <th>num_isocenters</th>\n","      <th>radiation_dosis</th>\n","      <th>isodosis</th>\n","      <th>curation</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>43.0</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>60.0</td>\n","      <td>...</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.372</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>17.0</td>\n","      <td>80.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.0</td>\n","      <td>25.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>90.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>3.0</td>\n","      <td>0.0</td>\n","      <td>7.670</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>13.0</td>\n","      <td>50.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>21.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>90.0</td>\n","      <td>...</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>2.040</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>18.0</td>\n","      <td>80.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.0</td>\n","      <td>29.0</td>\n","      <td>4.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>2.0</td>\n","      <td>90.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.256</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>17.0</td>\n","      <td>80.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.0</td>\n","      <td>24.0</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>90.0</td>\n","      <td>...</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.500</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>18.0</td>\n","      <td>80.0</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 33 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85fc8b87-de0d-4d85-bb76-0f3c3a5f582b')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 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Rodríguez Mallma","userId":"00922684507243278814"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"86c68ced-8bc6-432c-fa38-87474980aa32"},"source":["print(\"Dependant variable y: shape\", y.shape)\n","print(\"Indedendant variables X: shape \", X.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Dependant variable y: shape (151,)\n","Indedendant variables X: shape  (151, 32)\n"]}]},{"cell_type":"code","source":["X"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"ocvTbLfIsDzz","executionInfo":{"status":"ok","timestamp":1671590290558,"user_tz":300,"elapsed":76,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"outputId":"12d7250a-5405-47fd-9fa6-5a0c4c8d0d19"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     gender   age  ocupation  haemorrhage  headache  seizures  \\\n","0       0.0  43.0        3.0          1.0       1.0       0.0   \n","1       1.0  25.0        2.0          1.0       1.0       1.0   \n","2       1.0  21.0        2.0          1.0       1.0       1.0   \n","3       1.0  29.0        4.0          1.0       1.0       0.0   \n","4       0.0  24.0        3.0          1.0       0.0       0.0   \n","..      ...   ...        ...          ...       ...       ...   \n","146     1.0   5.0        6.0          1.0       1.0       0.0   \n","147     0.0  10.0        6.0          1.0       0.0       0.0   \n","148     1.0  19.0        5.0          1.0       1.0       1.0   \n","149     0.0  29.0        8.0          0.0       1.0       1.0   \n","150     1.0  18.0        7.0          0.0       1.0       1.0   \n","\n","     encephalomalacia  other_diseases  deficit  karnofsky_scale  ...  \\\n","0                 0.0             0.0      1.0             60.0  ...   \n","1                 0.0             0.0      1.0             90.0  ...   \n","2                 1.0             0.0      1.0             90.0  ...   \n","3                 1.0             0.0      2.0             90.0  ...   \n","4                 1.0             0.0      4.0             90.0  ...   \n","..                ...             ...      ...              ...  ...   \n","146               1.0             0.0      4.0             80.0  ...   \n","147               0.0             0.0      4.0             80.0  ...   \n","148               0.0             0.0      4.0            100.0  ...   \n","149               0.0             0.0      4.0             90.0  ...   \n","150               0.0             0.0      2.0             80.0  ...   \n","\n","     expansion_shape_avm  type_venous_drainage  eloquence  \\\n","0                    3.0                   2.0        1.0   \n","1                    2.0                   1.0        1.0   \n","2                    1.0                   3.0        1.0   \n","3                    2.0                   1.0        0.0   \n","4                    1.0                   2.0        0.0   \n","..                   ...                   ...        ...   \n","146                  2.0                   2.0        1.0   \n","147                  1.0                   2.0        1.0   \n","148                  3.0                   2.0        0.0   \n","149                  3.0                   2.0        1.0   \n","150                  1.0                   1.0        1.0   \n","\n","     blood_flow_velocity  venous_stenosis  volumen_avm  num_radiosurgeries  \\\n","0                    1.0              0.0        1.372                 1.0   \n","1                    3.0              0.0        7.670                 1.0   \n","2                    2.0              1.0        2.040                 1.0   \n","3                    1.0              0.0        0.256                 1.0   \n","4                    1.0              0.0        0.500                 1.0   \n","..                   ...              ...          ...                 ...   \n","146                  2.0              0.0        2.400                 1.0   \n","147                  3.0              1.0       17.900                 1.0   \n","148                  1.0              0.0        2.600                 1.0   \n","149                  2.0              1.0       12.000                 1.0   \n","150                  2.0              0.0        7.810                 1.0   \n","\n","     num_isocenters  radiation_dosis  isodosis  \n","0               1.0             17.0      80.0  \n","1               2.0             13.0      50.0  \n","2               1.0             18.0      80.0  \n","3               1.0             17.0      80.0  \n","4               1.0             18.0      80.0  \n","..              ...              ...       ...  \n","146             1.0             18.0      80.0  \n","147             2.0             15.0      50.0  \n","148             1.0             18.0      80.0  \n","149             1.0             18.0      80.0  \n","150             2.0             16.0      50.0  \n","\n","[151 rows x 32 columns]"],"text/html":["\n","  <div id=\"df-9f9baa61-fbce-42ee-b29e-3446c5f55999\">\n","    <div class=\"colab-df-container\">\n","      <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>gender</th>\n","      <th>age</th>\n","      <th>ocupation</th>\n","      <th>haemorrhage</th>\n","      <th>headache</th>\n","      <th>seizures</th>\n","      <th>encephalomalacia</th>\n","      <th>other_diseases</th>\n","      <th>deficit</th>\n","      <th>karnofsky_scale</th>\n","      <th>...</th>\n","      <th>expansion_shape_avm</th>\n","      <th>type_venous_drainage</th>\n","      <th>eloquence</th>\n","      <th>blood_flow_velocity</th>\n","      <th>venous_stenosis</th>\n","      <th>volumen_avm</th>\n","      <th>num_radiosurgeries</th>\n","      <th>num_isocenters</th>\n","      <th>radiation_dosis</th>\n","      <th>isodosis</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>43.0</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>60.0</td>\n","      <td>...</td>\n","      <td>3.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.372</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>17.0</td>\n","      <td>80.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.0</td>\n","      <td>25.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>90.0</td>\n","      <td>...</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>3.0</td>\n","      <td>0.0</td>\n","      <td>7.670</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>13.0</td>\n","      <td>50.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>21.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>90.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>2.040</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>18.0</td>\n","      <td>80.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.0</td>\n","      <td>29.0</td>\n","      <td>4.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>2.0</td>\n","      <td>90.0</td>\n","      <td>...</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.256</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>17.0</td>\n","      <td>80.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.0</td>\n","      <td>24.0</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>90.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.500</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>18.0</td>\n","      <td>80.0</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>146</th>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>6.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>80.0</td>\n","      <td>...</td>\n","      <td>2.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>2.400</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>18.0</td>\n","      <td>80.0</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>0.0</td>\n","      <td>10.0</td>\n","      <td>6.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>80.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>17.900</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>15.0</td>\n","      <td>50.0</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>1.0</td>\n","      <td>19.0</td>\n","      <td>5.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>100.0</td>\n","      <td>...</td>\n","      <td>3.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>2.600</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>18.0</td>\n","      <td>80.0</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>0.0</td>\n","      <td>29.0</td>\n","      <td>8.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>90.0</td>\n","      <td>...</td>\n","      <td>3.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>12.000</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>18.0</td>\n","      <td>80.0</td>\n","    </tr>\n","    <tr>\n","      <th>150</th>\n","      <td>1.0</td>\n","      <td>18.0</td>\n","      <td>7.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>2.0</td>\n","      <td>80.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>7.810</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>16.0</td>\n","      <td>50.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>151 rows × 32 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f9baa61-fbce-42ee-b29e-3446c5f55999')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 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Rodríguez Mallma","userId":"00922684507243278814"}},"outputId":"703ad047-0282-4229-c589-6f68e1577779"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['/content/drive/MyDrive/UNI/DSc/Publications/Mirko/Paper_Propuesta/Propuesta01/Modeling/model/obj/bal_scaler_20221221023812.ser']"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["X"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"Ad43087osPDL","executionInfo":{"status":"ok","timestamp":1671590290559,"user_tz":300,"elapsed":68,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"outputId":"e10440e3-0488-49e3-9d1a-a079d13e780a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     gender       age  ocupation  haemorrhage  headache  seizures  \\\n","0       0.0  0.609375   0.285714          1.0       1.0       0.0   \n","1       1.0  0.328125   0.142857          1.0       1.0       1.0   \n","2       1.0  0.265625   0.142857          1.0       1.0       1.0   \n","3       1.0  0.390625   0.428571          1.0       1.0       0.0   \n","4       0.0  0.312500   0.285714          1.0       0.0       0.0   \n","..      ...       ...        ...          ...       ...       ...   \n","146     1.0  0.015625   0.714286          1.0       1.0       0.0   \n","147     0.0  0.093750   0.714286          1.0       0.0       0.0   \n","148     1.0  0.234375   0.571429          1.0       1.0       1.0   \n","149     0.0  0.390625   1.000000          0.0       1.0       1.0   \n","150     1.0  0.218750   0.857143          0.0       1.0       1.0   \n","\n","     encephalomalacia  other_diseases   deficit  karnofsky_scale  ...  \\\n","0                 0.0             0.0  0.000000         0.333333  ...   \n","1                 0.0             0.0  0.000000         0.833333  ...   \n","2                 1.0             0.0  0.000000         0.833333  ...   \n","3                 1.0             0.0  0.333333         0.833333  ...   \n","4                 1.0             0.0  1.000000         0.833333  ...   \n","..                ...             ...       ...              ...  ...   \n","146               1.0             0.0  1.000000         0.666667  ...   \n","147               0.0             0.0  1.000000         0.666667  ...   \n","148               0.0             0.0  1.000000         1.000000  ...   \n","149               0.0             0.0  1.000000         0.833333  ...   \n","150               0.0             0.0  0.333333         0.666667  ...   \n","\n","     expansion_shape_avm  type_venous_drainage  eloquence  \\\n","0                    1.0                   0.5        1.0   \n","1                    0.5                   0.0        1.0   \n","2                    0.0                   1.0        1.0   \n","3                    0.5                   0.0        0.0   \n","4                    0.0                   0.5        0.0   \n","..                   ...                   ...        ...   \n","146                  0.5                   0.5        1.0   \n","147                  0.0                   0.5        1.0   \n","148                  1.0                   0.5        0.0   \n","149                  1.0                   0.5        1.0   \n","150                  0.0                   0.0        1.0   \n","\n","     blood_flow_velocity  venous_stenosis  volumen_avm  num_radiosurgeries  \\\n","0                    0.0              0.0     0.021142                 0.0   \n","1                    1.0              0.0     0.122825                 0.0   \n","2                    0.5              1.0     0.031927                 0.0   \n","3                    0.0              0.0     0.003124                 0.0   \n","4                    0.0              0.0     0.007064                 0.0   \n","..                   ...              ...          ...                 ...   \n","146                  0.5              0.0     0.037740                 0.0   \n","147                  1.0              1.0     0.287992                 0.0   \n","148                  0.0              0.0     0.040969                 0.0   \n","149                  0.5              1.0     0.192735                 0.0   \n","150                  0.5              0.0     0.125086                 0.0   \n","\n","     num_isocenters  radiation_dosis  isodosis  \n","0          0.000000         0.233333      0.75  \n","1          0.333333         0.100000      0.00  \n","2          0.000000         0.266667      0.75  \n","3          0.000000         0.233333      0.75  \n","4          0.000000         0.266667      0.75  \n","..              ...              ...       ...  \n","146        0.000000         0.266667      0.75  \n","147        0.333333         0.166667      0.00  \n","148        0.000000         0.266667      0.75  \n","149        0.000000         0.266667      0.75  \n","150        0.333333         0.200000      0.00  \n","\n","[151 rows x 32 columns]"],"text/html":["\n","  <div id=\"df-1470cc65-98c0-4c31-9d11-02974c99a8a8\">\n","    <div class=\"colab-df-container\">\n","      <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>gender</th>\n","      <th>age</th>\n","      <th>ocupation</th>\n","      <th>haemorrhage</th>\n","      <th>headache</th>\n","      <th>seizures</th>\n","      <th>encephalomalacia</th>\n","      <th>other_diseases</th>\n","      <th>deficit</th>\n","      <th>karnofsky_scale</th>\n","      <th>...</th>\n","      <th>expansion_shape_avm</th>\n","      <th>type_venous_drainage</th>\n","      <th>eloquence</th>\n","      <th>blood_flow_velocity</th>\n","      <th>venous_stenosis</th>\n","      <th>volumen_avm</th>\n","      <th>num_radiosurgeries</th>\n","      <th>num_isocenters</th>\n","      <th>radiation_dosis</th>\n","      <th>isodosis</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.0</td>\n","      <td>0.609375</td>\n","      <td>0.285714</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.333333</td>\n","      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Rodríguez Mallma","userId":"00922684507243278814"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"0e1521af-f09c-424b-bb41-f3d99e183731"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Train via Cross-Validation and Grid-Search for (Logistic-Regression) with Scoring (balanced_accuracy): \n","\n","\n","\n","Fold k: 1\n","Training set size: 132\n","Validanting set size: 19\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.652930, std: 0.069890 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.652930, std: 0.069890 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.736607, std: 0.109142 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.736607, std: 0.109142 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.480769, std: 0.050880 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.772550, std: 0.122139 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.480769, std: 0.050880 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.772550, std: 0.122139 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.748741, std: 0.092473 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.737523, std: 0.100613 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.748741, std: 0.092473 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.737523, std: 0.100613 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.763164, std: 0.097717 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.737523, std: 0.107298 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.763164, std: 0.097717 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.737523, std: 0.107298 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.725275, std: 0.122364 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.732715, std: 0.091038 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.725275, std: 0.122364 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.732715, std: 0.091038 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.720810, std: 0.130858 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.720810, std: 0.130858 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.720810, std: 0.130858 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.720810, std: 0.130858 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.772550 for params {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000,\n","                   solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (78.95%)\n","Sensitivity/Recall:  (81.25%)\n","Specificity:         (66.67%)\n","PPV:                 (92.86%)\n","NPV:                 (40.00%)\n","Balanced Accuracy:   (73.96%)\n","F1 Score:            (86.67%)\n","\n","\n","Fold k: 2\n","Training set size: 132\n","Validanting set size: 19\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.600160, std: 0.090879 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.600160, std: 0.090879 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.715774, std: 0.127219 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.715774, std: 0.127219 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.480769, std: 0.050880 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.779647, std: 0.142434 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.480769, std: 0.050880 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.779647, std: 0.142434 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.777358, std: 0.159440 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.721612, std: 0.149721 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.772550, std: 0.160575 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.721612, std: 0.149721 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.713370, std: 0.166348 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.718979, std: 0.148048 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.713370, std: 0.166348 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.718979, std: 0.148048 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.722299, std: 0.159632 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.744277, std: 0.107781 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.722299, std: 0.159632 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.744277, std: 0.107781 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.728251, std: 0.107888 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.749084, std: 0.110055 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.728251, std: 0.107888 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.749084, std: 0.110055 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.779647 for params {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000,\n","                   solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (89.47%)\n","Sensitivity/Recall:  (93.75%)\n","Specificity:         (66.67%)\n","PPV:                 (93.75%)\n","NPV:                 (66.67%)\n","Balanced Accuracy:   (80.21%)\n","F1 Score:            (93.75%)\n","\n","\n","Fold k: 3\n","Training set size: 132\n","Validanting set size: 19\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.582074, std: 0.061529 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.582074, std: 0.061529 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.744849, std: 0.095367 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.744849, std: 0.095367 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.755495, std: 0.115747 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.755495, std: 0.115747 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.719780, std: 0.113944 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.771749, std: 0.126200 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.719780, std: 0.113944 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.771749, std: 0.126200 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.768429, std: 0.186724 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.789606, std: 0.148721 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.768429, std: 0.186724 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.789606, std: 0.148721 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.768429, std: 0.120692 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.784799, std: 0.137426 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.768429, std: 0.120692 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.784799, std: 0.137426 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.742788, std: 0.167220 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.768429, std: 0.120692 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.742788, std: 0.167220 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.768429, std: 0.120692 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.789606 for params {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=10, class_weight='balanced', max_iter=1000,\n","                   solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (84.21%)\n","Sensitivity/Recall:  (93.75%)\n","Specificity:         (33.33%)\n","PPV:                 (88.24%)\n","NPV:                 (50.00%)\n","Balanced Accuracy:   (63.54%)\n","F1 Score:            (90.91%)\n","\n","\n","Fold k: 4\n","Training set size: 132\n","Validanting set size: 19\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.567880, std: 0.083475 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.567880, std: 0.083475 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.733974, std: 0.092010 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.733974, std: 0.092010 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.501603, std: 0.004240 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.769231, std: 0.134841 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.501603, std: 0.004240 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.769231, std: 0.134841 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.733745, std: 0.096180 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.763164, std: 0.127275 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.733745, std: 0.096180 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.763164, std: 0.127275 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.773924, std: 0.129332 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.692079, std: 0.122287 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.773924, std: 0.129332 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.692079, std: 0.122287 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.748283, std: 0.126919 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.757555, std: 0.108317 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.748283, std: 0.126919 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.757555, std: 0.108317 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.731914, std: 0.146651 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.757212, std: 0.128010 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.731914, std: 0.146651 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.757212, std: 0.128010 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.773924 for params {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=10, class_weight='balanced', max_iter=1000, penalty='l1',\n","                   solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (84.21%)\n","Sensitivity/Recall:  (81.25%)\n","Specificity:         (100.00%)\n","PPV:                 (100.00%)\n","NPV:                 (50.00%)\n","Balanced Accuracy:   (90.62%)\n","F1 Score:            (89.66%)\n","\n","\n","Fold k: 5\n","Training set size: 132\n","Validanting set size: 19\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.567995, std: 0.061134 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.567995, std: 0.061134 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.667926, std: 0.113780 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.667926, std: 0.113780 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.737294, std: 0.140011 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.737294, std: 0.140011 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.724245, std: 0.169584 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.748741, std: 0.125712 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.724245, std: 0.169584 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.748741, std: 0.125712 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.738668, std: 0.103174 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.740957, std: 0.089138 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.738668, std: 0.103174 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.740957, std: 0.089138 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.713370, std: 0.112104 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.724588, std: 0.101512 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.713370, std: 0.112104 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.724588, std: 0.101512 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.729739, std: 0.103787 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.724588, std: 0.074142 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.729739, std: 0.103787 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.724588, std: 0.074142 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.748741 for params {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=1, class_weight='balanced', max_iter=1000,\n","                   solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (78.95%)\n","Sensitivity/Recall:  (81.25%)\n","Specificity:         (66.67%)\n","PPV:                 (92.86%)\n","NPV:                 (40.00%)\n","Balanced Accuracy:   (73.96%)\n","F1 Score:            (86.67%)\n","\n","\n","Fold k: 6\n","Training set size: 132\n","Validanting set size: 19\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.594437, std: 0.068818 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.594437, std: 0.068818 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.695284, std: 0.124557 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.695284, std: 0.124557 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.719322, std: 0.123186 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.719322, std: 0.123186 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.751603, std: 0.144012 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.735234, std: 0.135865 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.751603, std: 0.144012 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.735234, std: 0.135865 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.793613, std: 0.128857 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.739698, std: 0.134510 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.793613, std: 0.128857 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.739698, std: 0.134510 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.741529, std: 0.086010 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.728136, std: 0.112754 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.741529, std: 0.086010 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.728136, std: 0.112754 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.716232, std: 0.133475 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.732601, std: 0.109962 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.716232, std: 0.133475 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.732601, std: 0.109962 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.793613 for params {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=10, class_weight='balanced', max_iter=1000, penalty='l1',\n","                   solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (73.68%)\n","Sensitivity/Recall:  (80.00%)\n","Specificity:         (50.00%)\n","PPV:                 (85.71%)\n","NPV:                 (40.00%)\n","Balanced Accuracy:   (65.00%)\n","F1 Score:            (82.76%)\n","\n","\n","Fold k: 7\n","Training set size: 132\n","Validanting set size: 19\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.575893, std: 0.054858 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.575893, std: 0.054858 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.761103, std: 0.099286 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.761103, std: 0.099286 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.806319, std: 0.123780 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.806319, std: 0.123780 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.851992, std: 0.159720 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.812614, std: 0.150601 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.851992, std: 0.159720 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.812614, std: 0.150601 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.762363, std: 0.144876 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.773924, std: 0.136269 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.762363, std: 0.144876 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.773924, std: 0.136269 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.721497, std: 0.190572 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.725962, std: 0.185665 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.721497, std: 0.190572 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.725962, std: 0.185665 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.700664, std: 0.207674 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.726305, std: 0.181290 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.705472, std: 0.204583 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.726305, std: 0.181290 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.851992 for params {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=1, class_weight='balanced', max_iter=1000, penalty='l1',\n","                   solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (68.42%)\n","Sensitivity/Recall:  (80.00%)\n","Specificity:         (25.00%)\n","PPV:                 (80.00%)\n","NPV:                 (25.00%)\n","Balanced Accuracy:   (52.50%)\n","F1 Score:            (80.00%)\n","\n","\n","Fold k: 8\n","Training set size: 133\n","Validanting set size: 18\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.615614, std: 0.141739 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.615614, std: 0.141739 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.736836, std: 0.103204 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.736836, std: 0.103204 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.800710, std: 0.104069 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.800710, std: 0.104069 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.788805, std: 0.110360 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.793613, std: 0.144920 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.788805, std: 0.110360 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.793613, std: 0.144920 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.811813, std: 0.130115 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.807005, std: 0.142637 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.811813, std: 0.130115 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.807005, std: 0.142637 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.811470, std: 0.106867 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.790980, std: 0.122895 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.811470, std: 0.106867 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.790980, std: 0.122895 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.801854, std: 0.118277 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.738897, std: 0.120628 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.801854, std: 0.118277 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.738897, std: 0.120628 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.811813 for params {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=10, class_weight='balanced', max_iter=1000, penalty='l1',\n","                   solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (88.89%)\n","Sensitivity/Recall:  (93.33%)\n","Specificity:         (66.67%)\n","PPV:                 (93.33%)\n","NPV:                 (66.67%)\n","Balanced Accuracy:   (80.00%)\n","F1 Score:            (93.33%)\n","\n","Train via Cross-Validation and Grid-Search for (Logistic-Regression) with Scoring (balanced_accuracy): \n","\n","\n","\n","Fold k: 1\n","Training set size: 218\n","Validanting set size: 32\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.596497, std: 0.054209 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.596497, std: 0.054209 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.831044, std: 0.058665 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.831044, std: 0.058665 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.759615, std: 0.044127 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.862294, std: 0.049095 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.759615, std: 0.044127 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.862294, std: 0.049095 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.889423, std: 0.036258 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884959, std: 0.054935 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.889423, std: 0.036258 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884959, std: 0.054935 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.911745, std: 0.057781 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884959, std: 0.053790 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.911745, std: 0.057781 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884959, std: 0.053790 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884615, std: 0.086276 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884959, std: 0.077988 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884615, std: 0.086276 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884959, std: 0.077988 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.889423, std: 0.068168 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884615, std: 0.074796 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.889423, std: 0.068168 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884615, std: 0.074796 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.911745 for params {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=10, max_iter=1000, penalty='l1', solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (87.50%)\n","Sensitivity/Recall:  (87.50%)\n","Specificity:         (87.50%)\n","PPV:                 (87.50%)\n","NPV:                 (87.50%)\n","Balanced Accuracy:   (87.50%)\n","F1 Score:            (87.50%)\n","\n","\n","Fold k: 2\n","Training set size: 218\n","Validanting set size: 32\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.583448, std: 0.056351 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.583448, std: 0.056351 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.821085, std: 0.075767 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.821085, std: 0.075767 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.750000, std: 0.031082 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.862981, std: 0.069057 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.750000, std: 0.031082 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.862981, std: 0.069057 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.876374, std: 0.081439 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.872253, std: 0.084252 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.876374, std: 0.081439 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.872253, std: 0.084252 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.885646, std: 0.078344 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.862981, std: 0.090986 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.885646, std: 0.078344 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.862981, std: 0.090986 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.843063, std: 0.087594 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.876030, std: 0.070758 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.843063, std: 0.087594 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.876030, std: 0.070758 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.843750, std: 0.099039 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.853022, std: 0.084932 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.848214, std: 0.092051 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.853022, std: 0.084932 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.885646 for params {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=10, max_iter=1000, penalty='l1', solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (100.00%)\n","Sensitivity/Recall:  (100.00%)\n","Specificity:         (100.00%)\n","PPV:                 (100.00%)\n","NPV:                 (100.00%)\n","Balanced Accuracy:   (100.00%)\n","F1 Score:            (100.00%)\n","\n","\n","Fold k: 3\n","Training set size: 219\n","Validanting set size: 31\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.587225, std: 0.037107 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.587225, std: 0.037107 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.822115, std: 0.037107 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.822115, std: 0.037107 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.737294, std: 0.062556 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.867445, std: 0.072448 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.737294, std: 0.062556 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.867445, std: 0.072448 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.880838, std: 0.063810 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.890110, std: 0.080095 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.880838, std: 0.063810 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.890110, std: 0.080095 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.881181, std: 0.076748 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.899725, std: 0.076972 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.881181, std: 0.076748 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.899725, std: 0.076972 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.894574, std: 0.086363 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.890110, std: 0.081589 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.894574, std: 0.086363 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.890110, std: 0.081589 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.899382, std: 0.079373 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.899382, std: 0.088851 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.894918, std: 0.088167 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.899382, std: 0.088851 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.899725 for params {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=10, max_iter=1000, solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (87.10%)\n","Sensitivity/Recall:  (87.50%)\n","Specificity:         (86.67%)\n","PPV:                 (87.50%)\n","NPV:                 (86.67%)\n","Balanced Accuracy:   (87.08%)\n","F1 Score:            (87.50%)\n","\n","\n","Fold k: 4\n","Training set size: 219\n","Validanting set size: 31\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.573489, std: 0.045532 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.573489, std: 0.045532 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.830357, std: 0.081494 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.830357, std: 0.081494 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.728365, std: 0.063580 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.853365, std: 0.051633 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.732830, std: 0.055674 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.853365, std: 0.051633 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884959, std: 0.063253 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.875687, std: 0.054596 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884959, std: 0.063253 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.875687, std: 0.054596 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884959, std: 0.085402 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.890110, std: 0.083836 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884959, std: 0.085402 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.890110, std: 0.083836 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.866415, std: 0.095770 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.866415, std: 0.083858 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.866415, std: 0.095770 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.866415, std: 0.083858 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.848214, std: 0.109372 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.861951, std: 0.093576 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.847871, std: 0.103851 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.861951, std: 0.093576 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.890110 for params {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=10, max_iter=1000, solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (90.32%)\n","Sensitivity/Recall:  (87.50%)\n","Specificity:         (93.33%)\n","PPV:                 (93.33%)\n","NPV:                 (87.50%)\n","Balanced Accuracy:   (90.42%)\n","F1 Score:            (90.32%)\n","\n","\n","Fold k: 5\n","Training set size: 219\n","Validanting set size: 31\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.582761, std: 0.048960 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.582761, std: 0.048960 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.807692, std: 0.039883 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.807692, std: 0.039883 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.741415, std: 0.056192 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.876030, std: 0.075442 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.741415, std: 0.056192 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.876030, std: 0.075442 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.898695, std: 0.067117 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884959, std: 0.084792 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.898695, std: 0.067117 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884959, std: 0.084792 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884615, std: 0.072269 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.894231, std: 0.078669 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.884615, std: 0.072269 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.894231, std: 0.078669 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.894231, std: 0.070257 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.898352, std: 0.067167 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.894231, std: 0.070257 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.898352, std: 0.067167 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.903503, std: 0.064698 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.903503, std: 0.067663 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.899038, std: 0.071121 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.903503, std: 0.067663 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.903503 for params {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=1000, max_iter=1000, penalty='l1', solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (87.10%)\n","Sensitivity/Recall:  (87.50%)\n","Specificity:         (86.67%)\n","PPV:                 (87.50%)\n","NPV:                 (86.67%)\n","Balanced Accuracy:   (87.08%)\n","F1 Score:            (87.50%)\n","\n","\n","Fold k: 6\n","Training set size: 219\n","Validanting set size: 31\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.604052, std: 0.031874 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.604052, std: 0.031874 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.835852, std: 0.051976 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.835852, std: 0.051976 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.762363, std: 0.082543 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.866758, std: 0.046744 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.762363, std: 0.082543 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.866758, std: 0.046744 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.881525, std: 0.091017 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.886332, std: 0.088243 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.881525, std: 0.091017 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.886332, std: 0.088243 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.877060, std: 0.080057 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.872253, std: 0.100030 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.881525, std: 0.079817 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.872253, std: 0.100030 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.862294, std: 0.096833 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.862981, std: 0.088206 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.862294, std: 0.096833 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.862981, std: 0.088206 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.858173, std: 0.084542 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.853709, std: 0.081598 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.858173, std: 0.084542 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.853709, std: 0.081598 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.886332 for params {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=1, max_iter=1000, solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (90.32%)\n","Sensitivity/Recall:  (80.00%)\n","Specificity:         (100.00%)\n","PPV:                 (100.00%)\n","NPV:                 (84.21%)\n","Balanced Accuracy:   (90.00%)\n","F1 Score:            (88.89%)\n","\n","\n","Fold k: 7\n","Training set size: 219\n","Validanting set size: 31\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.608860, std: 0.024454 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.608860, std: 0.024454 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.826923, std: 0.049374 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.826923, std: 0.049374 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.757212, std: 0.026097 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.863668, std: 0.073933 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.757212, std: 0.026097 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.863668, std: 0.073933 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.900069, std: 0.070964 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.886676, std: 0.071465 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.900069, std: 0.070964 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.886676, std: 0.071465 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.890453, std: 0.068522 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.872940, std: 0.088370 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.890453, std: 0.068522 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.872940, std: 0.088370 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.862637, std: 0.091075 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.871909, std: 0.081565 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.862637, std: 0.091075 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.871909, std: 0.081565 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.866758, std: 0.080811 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.871909, std: 0.082611 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.862294, std: 0.078362 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.871909, std: 0.082611 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.900069 for params {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=1, max_iter=1000, penalty='l1', solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (90.32%)\n","Sensitivity/Recall:  (86.67%)\n","Specificity:         (93.75%)\n","PPV:                 (92.86%)\n","NPV:                 (88.24%)\n","Balanced Accuracy:   (90.21%)\n","F1 Score:            (89.66%)\n","\n","\n","Fold k: 8\n","Training set size: 219\n","Validanting set size: 31\n","\n","Training Set: Looking for the best model ...\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.595467, std: 0.024982 --> {'C': 0.001, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.595467, std: 0.024982 --> {'C': 0.001, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.836882, std: 0.046194 --> {'C': 0.01, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.500000, std: 0.000000 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.836882, std: 0.046194 --> {'C': 0.01, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.740041, std: 0.058680 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.891140, std: 0.091229 --> {'C': 0.1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.740041, std: 0.058680 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.891140, std: 0.091229 --> {'C': 0.1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.913805, std: 0.050225 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.905220, std: 0.087114 --> {'C': 1, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.913805, std: 0.050225 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.905220, std: 0.087114 --> {'C': 1, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.914148, std: 0.058645 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.909341, std: 0.073319 --> {'C': 10, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.914148, std: 0.058645 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.909341, std: 0.073319 --> {'C': 10, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.909684, std: 0.046631 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.895948, std: 0.080230 --> {'C': 100, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.914148, std: 0.046516 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.895948, std: 0.080230 --> {'C': 100, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.905220, std: 0.052755 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.905220, std: 0.058488 --> {'C': 1000, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.900755, std: 0.048941 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\tbalanced_accuracy (train) | mean: 0.905220, std: 0.058488 --> {'C': 1000, 'max_iter': 5000, 'penalty': 'l2', 'solver': 'liblinear'}\n","\t> Best balanced_accuracy: 0.914148 for params {'C': 10, 'max_iter': 1000, 'penalty': 'l1', 'solver': 'liblinear'}\n","\t> Best model: LogisticRegression(C=10, max_iter=1000, penalty='l1', solver='liblinear')\n","\n","Validating Set: Making Predictions ...\n","Accuracy:            (87.10%)\n","Sensitivity/Recall:  (93.33%)\n","Specificity:         (81.25%)\n","PPV:                 (82.35%)\n","NPV:                 (92.86%)\n","Balanced Accuracy:   (87.29%)\n","F1 Score:            (87.50%)\n"]}]},{"cell_type":"markdown","metadata":{"id":"55Bc0nU7hAzL"},"source":["### Cross-Validation (ALL training data):"]},{"cell_type":"code","source":["# Imbalanced\n","best_model = crossValidation(X, y, False, 'accuracy', MODEL_NAME, N_FOLDS, DT_STRING, MODEL_FOLDER)\n","# Balanced\n","bal_best_model = crossValidation(X_bal, y_bal, True, 'accuracy', MODEL_NAME, N_FOLDS, DT_STRING, MODEL_FOLDER)"],"metadata":{"id":"cp_GD92AHknG","executionInfo":{"status":"ok","timestamp":1671590351558,"user_tz":300,"elapsed":3415,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"6181df56-1ac8-4566-ed4a-9c3074da926e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Cross-Validation for (Logistic-Regression) with Scoring (accuracy): \n","\n","  ModelID                                    ModelName      Mean  Variance\n","0       1  Logistic-Regression_1_20221221023812.joblib  0.794591  0.096643\n","1       2  Logistic-Regression_2_20221221023812.joblib  0.794591  0.096643\n","2       3  Logistic-Regression_3_20221221023812.joblib  0.827120  0.099409\n","3       4  Logistic-Regression_4_20221221023812.joblib  0.840643  0.075558\n","4       5  Logistic-Regression_5_20221221023812.joblib  0.813962  0.092856\n","5       6  Logistic-Regression_6_20221221023812.joblib  0.840643  0.075558\n","6       7  Logistic-Regression_7_20221221023812.joblib  0.820541  0.112952\n","7       8  Logistic-Regression_8_20221221023812.joblib  0.840643  0.075558\n","\n","Best model:  Logistic-Regression_4_20221221023812.joblib\n","\n","Cross-Validation for (Logistic-Regression) with Scoring (accuracy): \n","\n","  ModelID                                        ModelName      Mean  Variance\n","0       1  bal_Logistic-Regression_1_20221221023812.joblib  0.896799  0.065408\n","1       2  bal_Logistic-Regression_2_20221221023812.joblib  0.892767  0.063875\n","2       3  bal_Logistic-Regression_3_20221221023812.joblib  0.885081  0.089150\n","3       4  bal_Logistic-Regression_4_20221221023812.joblib  0.885081  0.089150\n","4       5  bal_Logistic-Regression_5_20221221023812.joblib  0.868826  0.080453\n","5       6  bal_Logistic-Regression_6_20221221023812.joblib  0.888861  0.069267\n","6       7  bal_Logistic-Regression_7_20221221023812.joblib  0.896799  0.072710\n","7       8  bal_Logistic-Regression_8_20221221023812.joblib  0.896799  0.065408\n","\n","Best model:  bal_Logistic-Regression_1_20221221023812.joblib\n"]}]},{"cell_type":"markdown","source":["## **Part III: Model Validation (TRAIN)**\n","---"],"metadata":{"id":"O6JQFJeNJ1EI"}},{"cell_type":"markdown","source":["### Loading models:"],"metadata":{"id":"mj9dkpDkvM_K"}},{"cell_type":"code","source":["# Imbalanced model\n","model_to_load = \"/\".join([MODEL_FOLDER, 'cv', best_model['ModelName'].values[0]])\n","print('Loading Model : %s' % model_to_load.split('/')[-1])\n","model = joblib.load(model_to_load)\n","y_pred = model.predict(X)\n","y_proba = model.predict_proba(X)[:,1]\n","\n","# Balanced model\n","model_to_load_bal = \"/\".join([MODEL_FOLDER, 'cv', bal_best_model['ModelName'].values[0]])\n","print('Loading Model : %s' % model_to_load_bal.split('/')[-1])\n","model_bal = joblib.load(model_to_load_bal)\n","y_pred_bal = model_bal.predict(X_bal)\n","y_proba_bal = model_bal.predict_proba(X_bal)[:,1]"],"metadata":{"id":"jEyBQGzxuY7p","executionInfo":{"status":"ok","timestamp":1671590351559,"user_tz":300,"elapsed":15,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"a1a5b56a-aa08-48b2-886f-8f41fbcf3ef9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Loading Model : Logistic-Regression_4_20221221023812.joblib\n","Loading Model : bal_Logistic-Regression_1_20221221023812.joblib\n"]}]},{"cell_type":"code","source":["# Confusion Matrix\n","plt.rcParams.update({'font.size': 20})\n","\n","f, axes = plt.subplots(1, 2, figsize=(12, 8), sharey='row')\n","cm = confusion_matrix(y, y_pred)\n","disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)\n","disp.plot(ax=axes[0], xticks_rotation=45, cmap=\"Blues\", colorbar=False)\n","disp.ax_.set_title('Imbalanced')\n","\n","cm_bal = confusion_matrix(y_bal, y_pred_bal)\n","disp_bal = ConfusionMatrixDisplay(confusion_matrix=cm_bal, display_labels=model_bal.classes_)\n","disp_bal.plot(ax=axes[1], xticks_rotation=45, cmap=\"Blues\", colorbar=False)\n","disp_bal.ax_.set_title('Balanced')\n","\n","f.text(0.5, 0.9, MODEL_NAME, ha='center')\n","plt.savefig(\"/\".join([OUTPUT_FOLDER,'graphs',MODEL_NAME + '_ConfusionMatrix-TRAIN_%s.jpg' % DT_STRING]), dpi=DPI_DEFAULT)\n","plt.show()\n","\n","\n","# Classification \n","print('\\nClassification Report %s (Imbalanced):' % MODEL_NAME)\n","print(classification_report(y, y_pred))\n","print('\\nClassification Report %s (Balanced):' % MODEL_NAME)\n","print(classification_report(y_bal, y_pred_bal))\n","\n","# Metrics\n","print('\\nMetrics %s (Imbalanced):' % MODEL_NAME)\n","metrics = getMetrics(cm, MODEL_NAME + ' Imbalanced', 'TRAIN')\n","auc = round(roc_auc_score(y, y_proba), 4)\n","metrics = [str(auc) if item == 'AUC' else item for item in metrics]\n","FINAL_REPORT_METRICS.append(metrics)\n","\n","print('\\nMetrics %s (Balanced):' % MODEL_NAME)\n","metrics_bal = getMetrics(cm_bal, MODEL_NAME + ' Balanced', 'TRAIN')\n","auc_bal = round(roc_auc_score(y_bal, y_proba_bal), 4)\n","metrics_bal = [str(auc_bal) if item == 'AUC' else item for item in metrics_bal]\n","FINAL_REPORT_METRICS.append(metrics_bal)\n","\n","# Ploting ROC Curve\n","print('\\nROC Curve:')\n","plt_1 = plt.figure(figsize=(12, 8))\n","plt.rcParams.update({'font.size': 15})\n","\n","fpr, tpr, thresholds = roc_curve(y, y_proba)\n","plt.plot(fpr,tpr,'-o', label=MODEL_NAME + ' Imbalanced ' + \" (auc=%.4f)\"  % (auc))\n","\n","fpr_bal, tpr_bal, thresholds_bal = roc_curve(y_bal, y_proba_bal)\n","plt.plot(fpr_bal,tpr_bal,'-o', label=MODEL_NAME + ' Balanced ' + \" (auc=%.4f)\"  % (auc_bal))\n","\n","plt.title(\"Receiving Operating Characteristic\")\n","plt.xlabel(\"False Positive Rate\")\n","plt.ylabel(\"True Positive Rate\")\n","plt.plot([0,1], 'r--')\n","plt.legend()\n","plt.savefig(\"/\".join([OUTPUT_FOLDER,'graphs',MODEL_NAME + '_ROC-TRAIN_%s.jpg' % DT_STRING]), dpi=DPI_DEFAULT)\n","\n","# print('\\nROC Curve:')\n","# plot_roc_curve(fpr, tpr, roc_auc_score(y, y_proba), MODEL_NAME)\n","# plot_roc_curve(fpr_bal, tpr_bal, roc_auc_score(y_bal, y_proba_bal), MODEL_NAME)"],"metadata":{"id":"akv0SuA_3GHQ","executionInfo":{"status":"ok","timestamp":1671590354399,"user_tz":300,"elapsed":2847,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"a1df4dcb-62e1-4889-b65a-4c9dd48e03a5"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 864x576 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["\n","Classification Report Logistic-Regression (Imbalanced):\n","              precision    recall  f1-score   support\n","\n","           0       0.84      1.00      0.91        26\n","           1       1.00      0.96      0.98       125\n","\n","    accuracy                           0.97       151\n","   macro avg       0.92      0.98      0.95       151\n","weighted avg       0.97      0.97      0.97       151\n","\n","\n","Classification Report Logistic-Regression (Balanced):\n","              precision    recall  f1-score   support\n","\n","           0       0.97      0.96      0.96       125\n","           1       0.96      0.97      0.96       125\n","\n","    accuracy                           0.96       250\n","   macro avg       0.96      0.96      0.96       250\n","weighted avg       0.96      0.96      0.96       250\n","\n","\n","Metrics Logistic-Regression (Imbalanced):\n","Accuracy:            (96.69%)\n","Sensitivity/Recall:  (96.00%)\n","Specificity:         (100.00%)\n","PPV:                 (100.00%)\n","NPV:                 (83.87%)\n","Balanced Accuracy:   (98.00%)\n","F1 Score:            (97.96%)\n","\n","Metrics Logistic-Regression (Balanced):\n","Accuracy:            (96.40%)\n","Sensitivity/Recall:  (96.80%)\n","Specificity:         (96.00%)\n","PPV:                 (96.03%)\n","NPV:                 (96.77%)\n","Balanced Accuracy:   (96.40%)\n","F1 Score:            (96.41%)\n","\n","ROC Curve:\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 864x576 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["## **Part IV: Model Evaluation (TEST - real data)**\n","---"],"metadata":{"id":"3i6H65vbdFnC"}},{"cell_type":"markdown","source":["### Getting X and Y (TEST):"],"metadata":{"id":"tOtRE3znqe6L"}},{"cell_type":"code","metadata":{"id":"gm04UuOhklGd","executionInfo":{"status":"ok","timestamp":1671590354400,"user_tz":300,"elapsed":9,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"6cc68c95-4577-40de-b125-9525f14cc24d"},"source":["# load test data\n","test_data_name_csv = 'test_set.csv';\n","test_data = pd.read_csv(\"/\".join([PROJECT_FOLDER, 'output', test_data_name_csv]))\n","\n","# Y\n","Y_TEST = test_data[CLASS].astype('int32')\n","\n","# X\n","X_COLUMNS = test_data.columns[:-1]\n","X_TEST_ORIG = test_data[X_COLUMNS].astype('float32')\n","\n","print(\"Test data: shape \",test_data.shape)\n","print(\"Dependant variable y: shape\", Y_TEST.shape)\n","print(\"Indedendant variables X: shape \", X_TEST_ORIG.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Test data: shape  (51, 33)\n","Dependant variable y: shape (51,)\n","Indedendant variables X: shape  (51, 32)\n"]}]},{"cell_type":"markdown","source":["### Loading models:"],"metadata":{"id":"dcEkvz0HvIyt"}},{"cell_type":"code","source":["# Imbalanced model\n","model_to_load = \"/\".join([MODEL_FOLDER, 'cv', best_model['ModelName'].values[0]])\n","print('Loading Model : %s' % model_to_load.split('/')[-1])\n","model = joblib.load(model_to_load)\n","the_scaler = joblib.load(\"/\".join([MODEL_FOLDER, \"obj\", \"scaler_\" + DT_STRING + \".ser\"]))\n","X_TEST = the_scaler.transform(X_TEST_ORIG).astype('float32')\n","Y_TEST_pred = model.predict(X_TEST)\n","Y_TEST_proba = model.predict_proba(X_TEST)[:,1]\n","\n","# Balanced model\n","model_to_load_bal = \"/\".join([MODEL_FOLDER, 'cv', bal_best_model['ModelName'].values[0]])\n","print('Loading Model : %s' % model_to_load_bal.split('/')[-1])\n","model_bal = joblib.load(model_to_load_bal)\n","the_bal_scaler = joblib.load(\"/\".join([MODEL_FOLDER, \"obj\", \"bal_scaler_\" + DT_STRING + \".ser\"]))\n","X_TEST_BAL = the_bal_scaler.transform(X_TEST_ORIG).astype('float32')\n","Y_TEST_pred_bal = model_bal.predict(X_TEST_BAL)\n","Y_TEST_proba_bal = model_bal.predict_proba(X_TEST_BAL)[:,1]"],"metadata":{"id":"TNToJtzWuk_O","executionInfo":{"status":"ok","timestamp":1671590354400,"user_tz":300,"elapsed":7,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"faf3e467-9b97-45f5-fc32-baeb28df330b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Loading Model : Logistic-Regression_4_20221221023812.joblib\n","Loading Model : bal_Logistic-Regression_1_20221221023812.joblib\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/sklearn/base.py:443: UserWarning: X has feature names, but MinMaxScaler was fitted without feature names\n","  warnings.warn(\n","/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.8/dist-packages/sklearn/base.py:443: UserWarning: X has feature names, but MinMaxScaler was fitted without feature names\n","  warnings.warn(\n","/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n","  warnings.warn(\n","/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n","  warnings.warn(\n"]}]},{"cell_type":"code","source":["# Confusion Matrix\n","plt.rcParams.update({'font.size': 20})\n","f, axes = plt.subplots(1, 2, figsize=(12, 8), sharey='row')\n","CM = confusion_matrix(Y_TEST, Y_TEST_pred)\n","disp = ConfusionMatrixDisplay(confusion_matrix=CM, display_labels=model.classes_)\n","disp.plot(ax=axes[0], xticks_rotation=45, cmap=\"Blues\", colorbar=False)\n","disp.ax_.set_title('Imbalanced Model')\n","\n","CM_bal = confusion_matrix(Y_TEST, Y_TEST_pred_bal)\n","disp_bal = ConfusionMatrixDisplay(confusion_matrix=CM_bal, display_labels=model_bal.classes_)\n","disp_bal.plot(ax=axes[1], xticks_rotation=45, cmap=\"Blues\", colorbar=False)\n","disp_bal.ax_.set_title('Balanced Model')\n","\n","f.text(0.5, 0.9, MODEL_NAME, ha='center')\n","plt.savefig(\"/\".join([OUTPUT_FOLDER,'graphs',MODEL_NAME + '_ConfusionMatrix-TEST_%s.jpg' % DT_STRING]), dpi=DPI_DEFAULT)\n","plt.show()\n","\n","\n","# Classification \n","print('\\nClassification Report %s (Imbalanced):' % MODEL_NAME)\n","print(classification_report(Y_TEST, Y_TEST_pred))\n","print('\\nClassification Report %s (Balanced):' % MODEL_NAME)\n","print(classification_report(Y_TEST, Y_TEST_pred_bal))\n","\n","# Metrics\n","print('\\nMetrics %s (Imbalanced):' % MODEL_NAME)\n","metrics = getMetrics(CM, MODEL_NAME + ' Imbalanced', 'TEST')\n","AUC = round(roc_auc_score(Y_TEST, Y_TEST_proba), 4)\n","metrics = [str(AUC) if item == 'AUC' else item for item in metrics]\n","FINAL_REPORT_METRICS.append(metrics)\n","\n","print('\\nMetrics %s (Balanced):' % MODEL_NAME)\n","metrics_bal = getMetrics(CM_bal, MODEL_NAME + ' Balanced ', 'TEST')\n","AUC_bal = round(roc_auc_score(Y_TEST, Y_TEST_proba_bal), 4)\n","metrics_bal = [str(AUC_bal) if item == 'AUC' else item for item in metrics_bal]\n","FINAL_REPORT_METRICS.append(metrics_bal)\n","\n","# Ploting ROC Curve\n","print('\\nROC Curve:')\n","plt_1 = plt.figure(figsize=(12, 8))\n","plt.rcParams.update({'font.size': 15})\n","\n","\n","fpr, tpr, thresholds = roc_curve(Y_TEST, Y_TEST_proba)\n","plt.plot(fpr,tpr,'-o', label=MODEL_NAME + ' Imbalanced ' + \" (auc=%.4f)\"  % (AUC))\n","\n","fpr_bal, tpr_bal, thresholds_bal = roc_curve(Y_TEST, Y_TEST_proba_bal)\n","plt.plot(fpr_bal,tpr_bal,'-o', label=MODEL_NAME + ' Balanced ' + \" (auc=%.4f)\"  % (AUC_bal))\n","\n","plt.title(\"Receiving Operating Characteristic\")\n","plt.xlabel(\"False Positive Rate\")\n","plt.ylabel(\"True Positive Rate\")\n","plt.plot([0,1], 'r--')\n","plt.legend()\n","plt.savefig(\"/\".join([OUTPUT_FOLDER,'graphs',MODEL_NAME + '_ROC-TEST_%s.jpg' % DT_STRING]), dpi=DPI_DEFAULT)\n","# print('\\nROC Curve:')\n","# plot_roc_curve(fpr, tpr, roc_auc_score(Y_TEST, Y_TEST_proba), MODEL_NAME)\n","# plot_roc_curve(fpr_bal, tpr_bal, roc_auc_score(Y_TEST, Y_TEST_proba_bal), MODEL_NAME)"],"metadata":{"id":"iYrfHoB3VvEk","executionInfo":{"status":"ok","timestamp":1671590358576,"user_tz":300,"elapsed":4180,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"77d7b464-27ff-4730-e28f-0fc7fc402b91"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 864x576 with 2 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["\n","Classification Report Logistic-Regression (Imbalanced):\n","              precision    recall  f1-score   support\n","\n","           0       0.54      0.78      0.64         9\n","           1       0.95      0.86      0.90        42\n","\n","    accuracy                           0.84        51\n","   macro avg       0.74      0.82      0.77        51\n","weighted avg       0.88      0.84      0.85        51\n","\n","\n","Classification Report Logistic-Regression (Balanced):\n","              precision    recall  f1-score   support\n","\n","           0       0.73      0.89      0.80         9\n","           1       0.97      0.93      0.95        42\n","\n","    accuracy                           0.92        51\n","   macro avg       0.85      0.91      0.88        51\n","weighted avg       0.93      0.92      0.92        51\n","\n","\n","Metrics Logistic-Regression (Imbalanced):\n","Accuracy:            (84.31%)\n","Sensitivity/Recall:  (85.71%)\n","Specificity:         (77.78%)\n","PPV:                 (94.74%)\n","NPV:                 (53.85%)\n","Balanced Accuracy:   (81.75%)\n","F1 Score:            (90.00%)\n","\n","Metrics Logistic-Regression (Balanced):\n","Accuracy:            (92.16%)\n","Sensitivity/Recall:  (92.86%)\n","Specificity:         (88.89%)\n","PPV:                 (97.50%)\n","NPV:                 (72.73%)\n","Balanced Accuracy:   (90.87%)\n","F1 Score:            (95.12%)\n","\n","ROC Curve:\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 864x576 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["## **Part V: Model Interpretation**\n","---"],"metadata":{"id":"mR2E9KltZOYT"}},{"cell_type":"code","source":["print('\\nImbalanced Model')\n","print('----------------')\n","w = model.coef_[0]\n","feature_importance = pd.DataFrame(X.columns, columns = [\"Feature\"])\n","feature_importance[\"Coef\"] = w\n","feature_importance[\"Importance\"] = pow(math.e, w)\n","feature_importance = feature_importance.sort_values(by = [\"Importance\"], ascending=False)\n","    \n","print(\"Intercept:\", model.intercept_[0])\n","print(feature_importance)\n","print('\\n')\n","\n","ax = feature_importance.plot.barh(x='Feature', y='Importance')\n","plt.show()"],"metadata":{"id":"QA2xbz2ZZXH2","executionInfo":{"status":"ok","timestamp":1671590359216,"user_tz":300,"elapsed":644,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"colab":{"base_uri":"https://localhost:8080/","height":950},"outputId":"523b0607-3f22-4f90-f62e-30b6cf187259"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Imbalanced Model\n","----------------\n","Intercept: 15.681924549696346\n","                 Feature      Coef  Importance\n","3            haemorrhage  5.452191  233.268802\n","2              ocupation  3.810102   45.155052\n","24             eloquence  3.421546   30.616699\n","5               seizures  2.794921   16.361333\n","21              side_avm  2.595646   13.405244\n","17     arterial_aneurysm  2.549134   12.796023\n","23  type_venous_drainage  1.680864    5.370194\n","30       radiation_dosis  0.613244    1.846412\n","28    num_radiosurgeries  0.000000    1.000000\n","1                    age  0.000000    1.000000\n","13     prev_cran_surgery  0.000000    1.000000\n","19  num_afferent_vessels  0.000000    1.000000\n","20             depth_avm  0.000000    1.000000\n","8                deficit  0.000000    1.000000\n","7         other_diseases  0.000000    1.000000\n","4               headache  0.000000    1.000000\n","9        karnofsky_scale  0.000000    1.000000\n","18       dolichoectasia  -0.430989    0.649866\n","16       venous_aneurysm -1.233949    0.291141\n","10    glasgow_coma_scale -1.532144    0.216072\n","0                 gender -2.214030    0.109259\n","15      localization_avm -2.220599    0.108544\n","14          embolization -3.635433    0.026373\n","26       venous_stenosis -4.316001    0.013353\n","6       encephalomalacia -4.878988    0.007605\n","11         buffalo_scale -5.449440    0.004299\n","31              isodosis -5.779350    0.003091\n","12        virginia_scale -5.788281    0.003063\n","29        num_isocenters -6.597996    0.001363\n","25   blood_flow_velocity -7.249803    0.000710\n","22   expansion_shape_avm -8.659583    0.000173\n","27           volumen_avm -9.023428    0.000121\n","\n","\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["print('\\nBalanced Model')\n","print('----------------')\n","w = model_bal.coef_[0]\n","feature_importance = pd.DataFrame(X.columns, columns = [\"Feature\"])\n","feature_importance[\"Coef\"] = w\n","feature_importance[\"Importance\"] = pow(math.e, w) #Odds ratio e^B\n","feature_importance = feature_importance.sort_values(by = [\"Importance\"], ascending=False)\n","\n","print(\"Intercept:\", model.intercept_[0])\n","print(feature_importance)\n","print('\\n')\n","plt.figure(figsize=(500,800))\n","ax = feature_importance.plot.barh(x='Feature', y='Importance')\n","plt.show()"],"metadata":{"id":"wXt0WVZ1cKax","executionInfo":{"status":"ok","timestamp":1671590360172,"user_tz":300,"elapsed":988,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"colab":{"base_uri":"https://localhost:8080/","height":967},"outputId":"821ed7da-f63a-4131-bed5-d7b411de3962"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Balanced Model\n","----------------\n","Intercept: 15.681924549696346\n","                 Feature       Coef    Importance\n","21              side_avm   3.685394  3.986084e+01\n","2              ocupation   3.684293  3.981696e+01\n","3            haemorrhage   3.612294  3.705096e+01\n","13     prev_cran_surgery   2.809908  1.660839e+01\n","23  type_venous_drainage   2.122742  8.354012e+00\n","8                deficit   1.009382  2.743904e+00\n","24             eloquence   0.977819  2.658651e+00\n","0                 gender   0.477996  1.612839e+00\n","5               seizures   0.427576  1.533536e+00\n","9        karnofsky_scale   0.000000  1.000000e+00\n","12        virginia_scale   0.000000  1.000000e+00\n","29        num_isocenters   0.000000  1.000000e+00\n","28    num_radiosurgeries   0.000000  1.000000e+00\n","17     arterial_aneurysm   0.000000  1.000000e+00\n","4               headache  -0.010291  9.897622e-01\n","10    glasgow_coma_scale  -0.063201  9.387549e-01\n","11         buffalo_scale  -0.593961  5.521360e-01\n","26       venous_stenosis  -1.082555  3.387290e-01\n","30       radiation_dosis  -1.114669  3.280240e-01\n","19  num_afferent_vessels  -1.227075  2.931488e-01\n","7         other_diseases  -1.294874  2.739323e-01\n","16       venous_aneurysm  -1.341657  2.614121e-01\n","1                    age  -2.233286  1.071757e-01\n","6       encephalomalacia  -3.069712  4.643451e-02\n","15      localization_avm  -3.108126  4.468462e-02\n","20             depth_avm  -3.231191  3.951041e-02\n","22   expansion_shape_avm  -4.010097  1.813164e-02\n","31              isodosis  -4.730437  8.822614e-03\n","14          embolization  -4.738869  8.748537e-03\n","18       dolichoectasia   -5.665930  3.461928e-03\n","25   blood_flow_velocity  -7.407565  6.066463e-04\n","27           volumen_avm -21.466891  4.753876e-10\n","\n","\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 36000x57600 with 0 Axes>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["## **Final Results:**"],"metadata":{"id":"RKsF0U-ecbav"}},{"cell_type":"code","source":["final_report = pd.DataFrame(FINAL_REPORT_METRICS, columns=FINAL_REPORT_METRICS_TITLE)\n","final_report.to_csv(\"/\".join([OUTPUT_FOLDER,'results', MODEL_NAME + '_results_' + DT_STRING + '.csv']), index=False) \n","final_report "],"metadata":{"id":"zlvj0WkRcibK","executionInfo":{"status":"ok","timestamp":1671590360174,"user_tz":300,"elapsed":11,"user":{"displayName":"Mirko J. Rodríguez Mallma","userId":"00922684507243278814"}},"colab":{"base_uri":"https://localhost:8080/","height":175},"outputId":"2de3383f-b0e9-4ae6-db49-724d3d04f312"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                       Model Name   Info Accuracy Sensitivity/Recall  \\\n","0  Logistic-Regression Imbalanced  TRAIN   0.9669             0.9600   \n","1    Logistic-Regression Balanced  TRAIN   0.9640             0.9680   \n","2  Logistic-Regression Imbalanced   TEST   0.8431             0.8571   \n","3   Logistic-Regression Balanced    TEST   0.9216             0.9286   \n","\n","  Specificity     PPV     NPV Balanced_accuracy      F1     AUC   TN  FP  FN  \\\n","0      1.0000  1.0000  0.8387            0.9800  0.9796  0.9923   26   0   5   \n","1      0.9600  0.9603  0.9677            0.9640  0.9641   0.991  120   5   4   \n","2      0.7778  0.9474  0.5385            0.8175  0.9000  0.9444    7   2   6   \n","3      0.8889  0.9750  0.7273            0.9087  0.9512  0.9762    8   1   3   \n","\n","    TP  \n","0  120  \n","1  121  \n","2   36  \n","3   39  "],"text/html":["\n","  <div id=\"df-2babd774-528f-407d-8448-6aec61766ec3\">\n","    <div class=\"colab-df-container\">\n","      <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>Model Name</th>\n","      <th>Info</th>\n","      <th>Accuracy</th>\n","      <th>Sensitivity/Recall</th>\n","      <th>Specificity</th>\n","      <th>PPV</th>\n","      <th>NPV</th>\n","      <th>Balanced_accuracy</th>\n","      <th>F1</th>\n","      <th>AUC</th>\n","      <th>TN</th>\n","      <th>FP</th>\n","      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    <td>TEST</td>\n","      <td>0.8431</td>\n","      <td>0.8571</td>\n","      <td>0.7778</td>\n","      <td>0.9474</td>\n","      <td>0.5385</td>\n","      <td>0.8175</td>\n","      <td>0.9000</td>\n","      <td>0.9444</td>\n","      <td>7</td>\n","      <td>2</td>\n","      <td>6</td>\n","      <td>36</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Logistic-Regression Balanced</td>\n","      <td>TEST</td>\n","      <td>0.9216</td>\n","      <td>0.9286</td>\n","      <td>0.8889</td>\n","      <td>0.9750</td>\n","      <td>0.7273</td>\n","      <td>0.9087</td>\n","      <td>0.9512</td>\n","      <td>0.9762</td>\n","      <td>8</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>39</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2babd774-528f-407d-8448-6aec61766ec3')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              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