Credit_Card_Default_Prediction_RF.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Credit_Card_Default_Prediction_RF.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1iHDva3wkq9fEzAA0VGkGsJM6RfFGPf4X","authorship_tag":"ABX9TyMBGpv2Ar1ZCKXsL40LeY75"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"iQBfEFSSiH-t","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":71},"outputId":"0f38f889-b97e-4dac-a123-3367fe634f8d","executionInfo":{"status":"ok","timestamp":1587344837169,"user_tz":300,"elapsed":1234,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}}},"source":["# Import the machine learning libraries.\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.model_selection import train_test_split\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import classification_report,confusion_matrix, accuracy_score\n","from sklearn import metrics\n","from sklearn.metrics import confusion_matrix\n","from mlxtend.plotting import plot_confusion_matrix\n","from sklearn.model_selection import KFold\n","from sklearn.model_selection import GridSearchCV\n","\n","%matplotlib inline\n","\n","# To ignore warnings\n","import warnings\n","warnings.filterwarnings(\"ignore\")"],"execution_count":1,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n","  import pandas.util.testing as tm\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"J_zWo5W9jfqp","colab_type":"code","outputId":"f8cd9bc5-9cb0-44c9-d54d-a6a1b41fdf21","executionInfo":{"status":"ok","timestamp":1587344839820,"user_tz":300,"elapsed":3730,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":224}},"source":["# Load the credit default dataset.\n","# The dataset can be downloaded here: http://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients \n","# OR https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset.\n","df = pd.read_csv('./drive/My Drive/ML_Code/Credit Default Prediction/credit-card-default.csv')\n","df.head()"],"execution_count":2,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>ID</th>\n","      <th>LIMIT_BAL</th>\n","      <th>SEX</th>\n","      <th>EDUCATION</th>\n","      <th>MARRIAGE</th>\n","      <th>AGE</th>\n","      <th>PAY_0</th>\n","      <th>PAY_2</th>\n","      <th>PAY_3</th>\n","      <th>PAY_4</th>\n","      <th>PAY_5</th>\n","      <th>PAY_6</th>\n","      <th>BILL_AMT1</th>\n","      <th>BILL_AMT2</th>\n","      <th>BILL_AMT3</th>\n","      <th>BILL_AMT4</th>\n","      <th>BILL_AMT5</th>\n","      <th>BILL_AMT6</th>\n","      <th>PAY_AMT1</th>\n","      <th>PAY_AMT2</th>\n","      <th>PAY_AMT3</th>\n","      <th>PAY_AMT4</th>\n","      <th>PAY_AMT5</th>\n","      <th>PAY_AMT6</th>\n","      <th>defaulted</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>20000</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>24</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>-2</td>\n","      <td>-2</td>\n","      <td>3913</td>\n","      <td>3102</td>\n","      <td>689</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>689</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>120000</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>26</td>\n","      <td>-1</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>2682</td>\n","      <td>1725</td>\n","      <td>2682</td>\n","      <td>3272</td>\n","      <td>3455</td>\n","      <td>3261</td>\n","      <td>0</td>\n","      <td>1000</td>\n","      <td>1000</td>\n","      <td>1000</td>\n","      <td>0</td>\n","      <td>2000</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>90000</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>34</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>29239</td>\n","      <td>14027</td>\n","      <td>13559</td>\n","      <td>14331</td>\n","      <td>14948</td>\n","      <td>15549</td>\n","      <td>1518</td>\n","      <td>1500</td>\n","      <td>1000</td>\n","      <td>1000</td>\n","      <td>1000</td>\n","      <td>5000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>50000</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>37</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>46990</td>\n","      <td>48233</td>\n","      <td>49291</td>\n","      <td>28314</td>\n","      <td>28959</td>\n","      <td>29547</td>\n","      <td>2000</td>\n","      <td>2019</td>\n","      <td>1200</td>\n","      <td>1100</td>\n","      <td>1069</td>\n","      <td>1000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>50000</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>57</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>-1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8617</td>\n","      <td>5670</td>\n","      <td>35835</td>\n","      <td>20940</td>\n","      <td>19146</td>\n","      <td>19131</td>\n","      <td>2000</td>\n","      <td>36681</td>\n","      <td>10000</td>\n","      <td>9000</td>\n","      <td>689</td>\n","      <td>679</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   ID  LIMIT_BAL  SEX  EDUCATION  ...  PAY_AMT4  PAY_AMT5  PAY_AMT6  defaulted\n","0   1      20000    2          2  ...         0         0         0          1\n","1   2     120000    2          2  ...      1000         0      2000          1\n","2   3      90000    2          2  ...      1000      1000      5000          0\n","3   4      50000    2          2  ...      1100      1069      1000          0\n","4   5      50000    1          2  ...      9000       689       679          0\n","\n","[5 rows x 25 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"DERNJgM0ovpF","colab_type":"code","outputId":"bf6c8909-6a4f-4838-a087-2b12a448f43a","executionInfo":{"status":"ok","timestamp":1587344839822,"user_tz":300,"elapsed":3695,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":561}},"source":["df.info()"],"execution_count":3,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 30000 entries, 0 to 29999\n","Data columns (total 25 columns):\n"," #   Column     Non-Null Count  Dtype\n","---  ------     --------------  -----\n"," 0   ID         30000 non-null  int64\n"," 1   LIMIT_BAL  30000 non-null  int64\n"," 2   SEX        30000 non-null  int64\n"," 3   EDUCATION  30000 non-null  int64\n"," 4   MARRIAGE   30000 non-null  int64\n"," 5   AGE        30000 non-null  int64\n"," 6   PAY_0      30000 non-null  int64\n"," 7   PAY_2      30000 non-null  int64\n"," 8   PAY_3      30000 non-null  int64\n"," 9   PAY_4      30000 non-null  int64\n"," 10  PAY_5      30000 non-null  int64\n"," 11  PAY_6      30000 non-null  int64\n"," 12  BILL_AMT1  30000 non-null  int64\n"," 13  BILL_AMT2  30000 non-null  int64\n"," 14  BILL_AMT3  30000 non-null  int64\n"," 15  BILL_AMT4  30000 non-null  int64\n"," 16  BILL_AMT5  30000 non-null  int64\n"," 17  BILL_AMT6  30000 non-null  int64\n"," 18  PAY_AMT1   30000 non-null  int64\n"," 19  PAY_AMT2   30000 non-null  int64\n"," 20  PAY_AMT3   30000 non-null  int64\n"," 21  PAY_AMT4   30000 non-null  int64\n"," 22  PAY_AMT5   30000 non-null  int64\n"," 23  PAY_AMT6   30000 non-null  int64\n"," 24  defaulted  30000 non-null  int64\n","dtypes: int64(25)\n","memory usage: 5.7 MB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"yuJHHCVRo08y","colab_type":"code","outputId":"073324bf-d3c3-47c1-a361-5cccc84ed87c","executionInfo":{"status":"ok","timestamp":1587344839823,"user_tz":300,"elapsed":3654,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":317}},"source":["df.describe()"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>ID</th>\n","      <th>LIMIT_BAL</th>\n","      <th>SEX</th>\n","      <th>EDUCATION</th>\n","      <th>MARRIAGE</th>\n","      <th>AGE</th>\n","      <th>PAY_0</th>\n","      <th>PAY_2</th>\n","      <th>PAY_3</th>\n","      <th>PAY_4</th>\n","      <th>PAY_5</th>\n","      <th>PAY_6</th>\n","      <th>BILL_AMT1</th>\n","      <th>BILL_AMT2</th>\n","      <th>BILL_AMT3</th>\n","      <th>BILL_AMT4</th>\n","      <th>BILL_AMT5</th>\n","      <th>BILL_AMT6</th>\n","      <th>PAY_AMT1</th>\n","      <th>PAY_AMT2</th>\n","      <th>PAY_AMT3</th>\n","      <th>PAY_AMT4</th>\n","      <th>PAY_AMT5</th>\n","      <th>PAY_AMT6</th>\n","      <th>defaulted</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>3.000000e+04</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>3.000000e+04</td>\n","      <td>30000.00000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","      <td>30000.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>15000.500000</td>\n","      <td>167484.322667</td>\n","      <td>1.603733</td>\n","      <td>1.853133</td>\n","      <td>1.551867</td>\n","      <td>35.485500</td>\n","      <td>-0.016700</td>\n","      <td>-0.133767</td>\n","      <td>-0.166200</td>\n","      <td>-0.220667</td>\n","      <td>-0.266200</td>\n","      <td>-0.291100</td>\n","      <td>51223.330900</td>\n","      <td>49179.075167</td>\n","      <td>4.701315e+04</td>\n","      <td>43262.948967</td>\n","      <td>40311.400967</td>\n","      <td>38871.760400</td>\n","      <td>5663.580500</td>\n","      <td>5.921163e+03</td>\n","      <td>5225.68150</td>\n","      <td>4826.076867</td>\n","      <td>4799.387633</td>\n","      <td>5215.502567</td>\n","      <td>0.221200</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>8660.398374</td>\n","      <td>129747.661567</td>\n","      <td>0.489129</td>\n","      <td>0.790349</td>\n","      <td>0.521970</td>\n","      <td>9.217904</td>\n","      <td>1.123802</td>\n","      <td>1.197186</td>\n","      <td>1.196868</td>\n","      <td>1.169139</td>\n","      <td>1.133187</td>\n","      <td>1.149988</td>\n","      <td>73635.860576</td>\n","      <td>71173.768783</td>\n","      <td>6.934939e+04</td>\n","      <td>64332.856134</td>\n","      <td>60797.155770</td>\n","      <td>59554.107537</td>\n","      <td>16563.280354</td>\n","      <td>2.304087e+04</td>\n","      <td>17606.96147</td>\n","      <td>15666.159744</td>\n","      <td>15278.305679</td>\n","      <td>17777.465775</td>\n","      <td>0.415062</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>10000.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>21.000000</td>\n","      <td>-2.000000</td>\n","      <td>-2.000000</td>\n","      <td>-2.000000</td>\n","      <td>-2.000000</td>\n","      <td>-2.000000</td>\n","      <td>-2.000000</td>\n","      <td>-165580.000000</td>\n","      <td>-69777.000000</td>\n","      <td>-1.572640e+05</td>\n","      <td>-170000.000000</td>\n","      <td>-81334.000000</td>\n","      <td>-339603.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000e+00</td>\n","      <td>0.00000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>7500.750000</td>\n","      <td>50000.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>28.000000</td>\n","      <td>-1.000000</td>\n","      <td>-1.000000</td>\n","      <td>-1.000000</td>\n","      <td>-1.000000</td>\n","      <td>-1.000000</td>\n","      <td>-1.000000</td>\n","      <td>3558.750000</td>\n","      <td>2984.750000</td>\n","      <td>2.666250e+03</td>\n","      <td>2326.750000</td>\n","      <td>1763.000000</td>\n","      <td>1256.000000</td>\n","      <td>1000.000000</td>\n","      <td>8.330000e+02</td>\n","      <td>390.00000</td>\n","      <td>296.000000</td>\n","      <td>252.500000</td>\n","      <td>117.750000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>15000.500000</td>\n","      <td>140000.000000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>34.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>22381.500000</td>\n","      <td>21200.000000</td>\n","      <td>2.008850e+04</td>\n","      <td>19052.000000</td>\n","      <td>18104.500000</td>\n","      <td>17071.000000</td>\n","      <td>2100.000000</td>\n","      <td>2.009000e+03</td>\n","      <td>1800.00000</td>\n","      <td>1500.000000</td>\n","      <td>1500.000000</td>\n","      <td>1500.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>22500.250000</td>\n","      <td>240000.000000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>41.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>67091.000000</td>\n","      <td>64006.250000</td>\n","      <td>6.016475e+04</td>\n","      <td>54506.000000</td>\n","      <td>50190.500000</td>\n","      <td>49198.250000</td>\n","      <td>5006.000000</td>\n","      <td>5.000000e+03</td>\n","      <td>4505.00000</td>\n","      <td>4013.250000</td>\n","      <td>4031.500000</td>\n","      <td>4000.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>30000.000000</td>\n","      <td>1000000.000000</td>\n","      <td>2.000000</td>\n","      <td>6.000000</td>\n","      <td>3.000000</td>\n","      <td>79.000000</td>\n","      <td>8.000000</td>\n","      <td>8.000000</td>\n","      <td>8.000000</td>\n","      <td>8.000000</td>\n","      <td>8.000000</td>\n","      <td>8.000000</td>\n","      <td>964511.000000</td>\n","      <td>983931.000000</td>\n","      <td>1.664089e+06</td>\n","      <td>891586.000000</td>\n","      <td>927171.000000</td>\n","      <td>961664.000000</td>\n","      <td>873552.000000</td>\n","      <td>1.684259e+06</td>\n","      <td>896040.00000</td>\n","      <td>621000.000000</td>\n","      <td>426529.000000</td>\n","      <td>528666.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 ID       LIMIT_BAL  ...       PAY_AMT6     defaulted\n","count  30000.000000    30000.000000  ...   30000.000000  30000.000000\n","mean   15000.500000   167484.322667  ...    5215.502567      0.221200\n","std     8660.398374   129747.661567  ...   17777.465775      0.415062\n","min        1.000000    10000.000000  ...       0.000000      0.000000\n","25%     7500.750000    50000.000000  ...     117.750000      0.000000\n","50%    15000.500000   140000.000000  ...    1500.000000      0.000000\n","75%    22500.250000   240000.000000  ...    4000.000000      0.000000\n","max    30000.000000  1000000.000000  ...  528666.000000      1.000000\n","\n","[8 rows x 25 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"D2J4Uh-To2i_","colab_type":"code","colab":{}},"source":["# Split a dataset into X and y.\n","X = df.drop('defaulted',axis=1)\n","y = df['defaulted']"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cfUT7by3pI1k","colab_type":"code","colab":{}},"source":["# Split the dataset into the training set and test subsets.\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"QesLTJgWphLS","colab_type":"code","outputId":"6d489188-0e10-4e59-a6cf-9f018e88cbf4","executionInfo":{"status":"ok","timestamp":1587344839827,"user_tz":300,"elapsed":3575,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Run the random forest with default parameters.\n","%%time\n","rfc = RandomForestClassifier()"],"execution_count":7,"outputs":[{"output_type":"stream","text":["CPU times: user 39 µs, sys: 9 µs, total: 48 µs\n","Wall time: 52.5 µs\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"wbz8GBieqDdE","colab_type":"code","outputId":"8c04ecc3-1123-4af7-d559-7f697a789efa","executionInfo":{"status":"ok","timestamp":1587344845602,"user_tz":300,"elapsed":9281,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["# Fit the random forest model.\n","%%time\n","rfc.fit(X_train, y_train)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["CPU times: user 5.49 s, sys: 4.55 ms, total: 5.49 s\n","Wall time: 5.5 s\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n","                       criterion='gini', max_depth=None, max_features='auto',\n","                       max_leaf_nodes=None, max_samples=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=1, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, n_estimators=100,\n","                       n_jobs=None, oob_score=False, random_state=None,\n","                       verbose=0, warm_start=False)"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"ukEedtY5qOIC","colab_type":"code","outputId":"77cf5672-b4cd-4d5c-9a72-47ed17bc7416","executionInfo":{"status":"ok","timestamp":1587344845605,"user_tz":300,"elapsed":9221,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Make predictions.\n","%%time\n","y_pred = rfc.predict(X_test)"],"execution_count":9,"outputs":[{"output_type":"stream","text":["CPU times: user 178 ms, sys: 886 µs, total: 179 ms\n","Wall time: 179 ms\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"8MYHNCMpqlLv","colab_type":"code","outputId":"3344f9b2-32cf-4cc4-b748-d93808891fe9","executionInfo":{"status":"ok","timestamp":1587344845860,"user_tz":300,"elapsed":9444,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":170}},"source":["# Print classification report.\n","print(classification_report(y_test, y_pred))"],"execution_count":10,"outputs":[{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.84      0.95      0.89      7060\n","           1       0.66      0.37      0.47      1940\n","\n","    accuracy                           0.82      9000\n","   macro avg       0.75      0.66      0.68      9000\n","weighted avg       0.80      0.82      0.80      9000\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jlnBnr21qxg5","colab_type":"code","outputId":"9d7d59ec-18c8-4bc2-e4fe-45de858571ff","executionInfo":{"status":"ok","timestamp":1587344845863,"user_tz":300,"elapsed":9423,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":500}},"source":["# Plot the confusion matrix.\n","%%time\n","cm = metrics.confusion_matrix(y_true=y_test, y_pred=y_pred)\n","plot_confusion_matrix(conf_mat=cm,\n","                      show_absolute=True,\n","                      show_normed=True,\n","                      colorbar=True,\n","                      figsize=(8,8))\n","plt.show()"],"execution_count":11,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x576 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["CPU times: user 173 ms, sys: 6.02 ms, total: 179 ms\n","Wall time: 201 ms\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"mGMH8Smrq7S0","colab_type":"code","outputId":"9a92d29d-fc6c-4db3-b129-a62e2fd8adc9","executionInfo":{"status":"ok","timestamp":1587344845868,"user_tz":300,"elapsed":9409,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Print accuracy.\n","print(\"Accuracy:\", metrics.accuracy_score(y_true=y_test, y_pred=y_pred), \"\\n\")"],"execution_count":12,"outputs":[{"output_type":"stream","text":["Accuracy: 0.8223333333333334 \n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"vzpV4G60rNg1","colab_type":"text"},"source":["### **Hyperparameter Tuning**"]},{"cell_type":"code","metadata":{"id":"2eoSQCbqrKze","colab_type":"code","outputId":"594ac470-1588-4107-bba0-7f5fe28b4cf6","executionInfo":{"status":"ok","timestamp":1587344953342,"user_tz":300,"elapsed":116854,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Create a KFold object with 10 splits.\n","%%time\n","n_folds = 10\n","\n","# Specify range of hyperparameters.\n","# Set the parameters by cross-validation\n","hyper_params = {'max_depth': range(2, 20, 5)}\n","\n","# Random Forest model\n","rfc = RandomForestClassifier()\n","\n","# Set up GridSearchCV() funcion.\n","rfc = GridSearchCV(rfc, hyper_params, cv=n_folds, scoring=\"accuracy\")\n","\n","# Fit the model.\n","rfc.fit(X_train, y_train)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["CPU times: user 1min 47s, sys: 58.1 ms, total: 1min 47s\n","Wall time: 1min 47s\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"VqqSErRrtAAk","colab_type":"code","outputId":"3e5d3757-1b0b-4ac7-e8c5-ca3acc9ec6e1","executionInfo":{"status":"ok","timestamp":1587344953344,"user_tz":300,"elapsed":116839,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":261}},"source":["# Cross-Validation results.\n","cv_results = pd.DataFrame(rfc.cv_results_)\n","cv_results"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>mean_fit_time</th>\n","      <th>std_fit_time</th>\n","      <th>mean_score_time</th>\n","      <th>std_score_time</th>\n","      <th>param_max_depth</th>\n","      <th>params</th>\n","      <th>split0_test_score</th>\n","      <th>split1_test_score</th>\n","      <th>split2_test_score</th>\n","      <th>split3_test_score</th>\n","      <th>split4_test_score</th>\n","      <th>split5_test_score</th>\n","      <th>split6_test_score</th>\n","      <th>split7_test_score</th>\n","      <th>split8_test_score</th>\n","      <th>split9_test_score</th>\n","      <th>mean_test_score</th>\n","      <th>std_test_score</th>\n","      <th>rank_test_score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.840982</td>\n","      <td>0.010130</td>\n","      <td>0.016223</td>\n","      <td>0.002843</td>\n","      <td>2</td>\n","      <td>{'max_depth': 2}</td>\n","      <td>0.806667</td>\n","      <td>0.805714</td>\n","      <td>0.801905</td>\n","      <td>0.800952</td>\n","      <td>0.802857</td>\n","      <td>0.793810</td>\n","      <td>0.794762</td>\n","      <td>0.796667</td>\n","      <td>0.797619</td>\n","      <td>0.798095</td>\n","      <td>0.799905</td>\n","      <td>0.004194</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.107498</td>\n","      <td>0.013382</td>\n","      <td>0.022244</td>\n","      <td>0.001183</td>\n","      <td>7</td>\n","      <td>{'max_depth': 7}</td>\n","      <td>0.823810</td>\n","      <td>0.824286</td>\n","      <td>0.826190</td>\n","      <td>0.820476</td>\n","      <td>0.819048</td>\n","      <td>0.810952</td>\n","      <td>0.816667</td>\n","      <td>0.807619</td>\n","      <td>0.804286</td>\n","      <td>0.822381</td>\n","      <td>0.817571</td>\n","      <td>0.007157</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.235987</td>\n","      <td>0.031495</td>\n","      <td>0.032840</td>\n","      <td>0.002015</td>\n","      <td>12</td>\n","      <td>{'max_depth': 12}</td>\n","      <td>0.823810</td>\n","      <td>0.821429</td>\n","      <td>0.826190</td>\n","      <td>0.819524</td>\n","      <td>0.821429</td>\n","      <td>0.813333</td>\n","      <td>0.816190</td>\n","      <td>0.808571</td>\n","      <td>0.810476</td>\n","      <td>0.820000</td>\n","      <td>0.818095</td>\n","      <td>0.005488</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.066148</td>\n","      <td>0.046652</td>\n","      <td>0.043365</td>\n","      <td>0.000529</td>\n","      <td>17</td>\n","      <td>{'max_depth': 17}</td>\n","      <td>0.820952</td>\n","      <td>0.817143</td>\n","      <td>0.819048</td>\n","      <td>0.823810</td>\n","      <td>0.816667</td>\n","      <td>0.808095</td>\n","      <td>0.816190</td>\n","      <td>0.810476</td>\n","      <td>0.814286</td>\n","      <td>0.820952</td>\n","      <td>0.816762</td>\n","      <td>0.004596</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score\n","0       0.840982      0.010130  ...        0.004194                4\n","1       2.107498      0.013382  ...        0.007157                2\n","2       3.235987      0.031495  ...        0.005488                1\n","3       4.066148      0.046652  ...        0.004596                3\n","\n","[4 rows x 19 columns]"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"-gwDOpvRtQIi","colab_type":"code","outputId":"dd8bbcc4-e3fb-4fe4-8476-b8cf28b498ea","executionInfo":{"status":"ok","timestamp":1587344953346,"user_tz":300,"elapsed":116829,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["# Plot test accuracies with max_depth.\n","plt.figure(figsize=(6,4))\n","plt.plot(cv_results[\"param_max_depth\"], cv_results[\"mean_test_score\"], label=\"Test accuracy\", linewidth=2.5)\n","plt.xlabel(\"max_depth\", fontsize=16)\n","plt.ylabel(\"Accuracy\", fontsize=16)\n","plt.legend()\n","plt.show()"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"0w-W5dn_tuSu","colab_type":"code","outputId":"3789094c-6983-4d3f-b566-911dc617a3ee","executionInfo":{"status":"ok","timestamp":1587345472031,"user_tz":300,"elapsed":635500,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Create a KFold object with 10 splits.\n","%%time\n","n_folds = 10\n","\n","# Specify range of hyperparameters.\n","# Set the parameters by cross-validation\n","hyper_params = {'n_estimators': range(100, 1500, 400)}\n","\n","# Random Forest model\n","rfc = RandomForestClassifier(max_depth=6)\n","\n","# Set up GridSearchCV() funcion.\n","rfc = GridSearchCV(rfc, hyper_params, cv=n_folds, scoring=\"accuracy\")\n","\n","# Fit the model.\n","rfc.fit(X_train, y_train)"],"execution_count":16,"outputs":[{"output_type":"stream","text":["CPU times: user 8min 38s, sys: 217 ms, total: 8min 38s\n","Wall time: 8min 38s\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"pjARL9ExvoTz","colab_type":"code","outputId":"5c6cc0a6-b2a0-4ab5-b30c-34bd50a44e6b","executionInfo":{"status":"ok","timestamp":1587345472040,"user_tz":300,"elapsed":635495,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":261}},"source":["# Cross-Validation results.\n","cv_results = pd.DataFrame(rfc.cv_results_)\n","cv_results"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>mean_fit_time</th>\n","      <th>std_fit_time</th>\n","      <th>mean_score_time</th>\n","      <th>std_score_time</th>\n","      <th>param_n_estimators</th>\n","      <th>params</th>\n","      <th>split0_test_score</th>\n","      <th>split1_test_score</th>\n","      <th>split2_test_score</th>\n","      <th>split3_test_score</th>\n","      <th>split4_test_score</th>\n","      <th>split5_test_score</th>\n","      <th>split6_test_score</th>\n","      <th>split7_test_score</th>\n","      <th>split8_test_score</th>\n","      <th>split9_test_score</th>\n","      <th>mean_test_score</th>\n","      <th>std_test_score</th>\n","      <th>rank_test_score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.848071</td>\n","      <td>0.025832</td>\n","      <td>0.020884</td>\n","      <td>0.001738</td>\n","      <td>100</td>\n","      <td>{'n_estimators': 100}</td>\n","      <td>0.821429</td>\n","      <td>0.819524</td>\n","      <td>0.821429</td>\n","      <td>0.819524</td>\n","      <td>0.817143</td>\n","      <td>0.808571</td>\n","      <td>0.807143</td>\n","      <td>0.806667</td>\n","      <td>0.811429</td>\n","      <td>0.817143</td>\n","      <td>0.815000</td>\n","      <td>0.005639</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>9.138484</td>\n","      <td>0.052183</td>\n","      <td>0.094162</td>\n","      <td>0.001574</td>\n","      <td>500</td>\n","      <td>{'n_estimators': 500}</td>\n","      <td>0.821905</td>\n","      <td>0.820952</td>\n","      <td>0.818095</td>\n","      <td>0.813810</td>\n","      <td>0.817143</td>\n","      <td>0.810476</td>\n","      <td>0.808571</td>\n","      <td>0.808095</td>\n","      <td>0.810476</td>\n","      <td>0.818571</td>\n","      <td>0.814810</td>\n","      <td>0.004909</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>16.432984</td>\n","      <td>0.059960</td>\n","      <td>0.173480</td>\n","      <td>0.019832</td>\n","      <td>900</td>\n","      <td>{'n_estimators': 900}</td>\n","      <td>0.820476</td>\n","      <td>0.821429</td>\n","      <td>0.819048</td>\n","      <td>0.815238</td>\n","      <td>0.819524</td>\n","      <td>0.810952</td>\n","      <td>0.809048</td>\n","      <td>0.807143</td>\n","      <td>0.804762</td>\n","      <td>0.822381</td>\n","      <td>0.815000</td>\n","      <td>0.006173</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>23.689813</td>\n","      <td>0.080145</td>\n","      <td>0.246046</td>\n","      <td>0.009909</td>\n","      <td>1300</td>\n","      <td>{'n_estimators': 1300}</td>\n","      <td>0.821905</td>\n","      <td>0.820000</td>\n","      <td>0.820000</td>\n","      <td>0.815238</td>\n","      <td>0.817143</td>\n","      <td>0.810476</td>\n","      <td>0.808571</td>\n","      <td>0.808571</td>\n","      <td>0.803810</td>\n","      <td>0.820476</td>\n","      <td>0.814619</td>\n","      <td>0.005993</td>\n","      <td>4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score\n","0       1.848071      0.025832  ...        0.005639                1\n","1       9.138484      0.052183  ...        0.004909                3\n","2      16.432984      0.059960  ...        0.006173                1\n","3      23.689813      0.080145  ...        0.005993                4\n","\n","[4 rows x 19 columns]"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"id":"t5LMJ8jGvsVt","colab_type":"code","outputId":"77199bf2-ea24-4ca1-a17e-400e973c2325","executionInfo":{"status":"ok","timestamp":1587345472042,"user_tz":300,"elapsed":635483,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["# Plot test accuracies with max_depth.\n","plt.figure(figsize=(6,4))\n","plt.plot(cv_results[\"param_n_estimators\"], cv_results[\"mean_test_score\"], label=\"Test accuracy\", linewidth=2.5)\n","plt.xlabel(\"n_estimators\", fontsize=16)\n","plt.ylabel(\"Accuracy\", fontsize=16)\n","plt.legend()\n","plt.show()"],"execution_count":18,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"uap5RS5Av2cq","colab_type":"code","outputId":"edb2ccac-0495-45f9-d76a-c68f0245ef70","executionInfo":{"status":"ok","timestamp":1587345764447,"user_tz":300,"elapsed":927864,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Create a KFold object with 10 splits.\n","%%time\n","n_folds = 10\n","\n","# Specify range of hyperparameters.\n","# Set the parameters by cross-validation\n","hyper_params = {'max_features': [4, 8, 14, 20, 24]}\n","\n","# Random Forest model\n","rfc = RandomForestClassifier(max_depth=6)\n","\n","# Set up GridSearchCV() funcion.\n","rfc = GridSearchCV(rfc, hyper_params, cv=n_folds, scoring=\"accuracy\")\n","\n","# Fit the model.\n","rfc.fit(X_train, y_train)"],"execution_count":19,"outputs":[{"output_type":"stream","text":["CPU times: user 4min 51s, sys: 52 ms, total: 4min 51s\n","Wall time: 4min 52s\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Df60OP0UwCVr","colab_type":"code","outputId":"d5c513d3-e98b-47d0-f939-4334b3674dfe","executionInfo":{"status":"ok","timestamp":1587345764452,"user_tz":300,"elapsed":927846,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":309}},"source":["# Cross-Validation results.\n","cv_results = pd.DataFrame(rfc.cv_results_)\n","cv_results"],"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>mean_fit_time</th>\n","      <th>std_fit_time</th>\n","      <th>mean_score_time</th>\n","      <th>std_score_time</th>\n","      <th>param_max_features</th>\n","      <th>params</th>\n","      <th>split0_test_score</th>\n","      <th>split1_test_score</th>\n","      <th>split2_test_score</th>\n","      <th>split3_test_score</th>\n","      <th>split4_test_score</th>\n","      <th>split5_test_score</th>\n","      <th>split6_test_score</th>\n","      <th>split7_test_score</th>\n","      <th>split8_test_score</th>\n","      <th>split9_test_score</th>\n","      <th>mean_test_score</th>\n","      <th>std_test_score</th>\n","      <th>rank_test_score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.846149</td>\n","      <td>0.020484</td>\n","      <td>0.025296</td>\n","      <td>0.015475</td>\n","      <td>4</td>\n","      <td>{'max_features': 4}</td>\n","      <td>0.820952</td>\n","      <td>0.817619</td>\n","      <td>0.822857</td>\n","      <td>0.816190</td>\n","      <td>0.815714</td>\n","      <td>0.810000</td>\n","      <td>0.810000</td>\n","      <td>0.806667</td>\n","      <td>0.806667</td>\n","      <td>0.820476</td>\n","      <td>0.814714</td>\n","      <td>0.005692</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>3.335710</td>\n","      <td>0.014387</td>\n","      <td>0.020863</td>\n","      <td>0.001847</td>\n","      <td>8</td>\n","      <td>{'max_features': 8}</td>\n","      <td>0.824286</td>\n","      <td>0.822857</td>\n","      <td>0.825714</td>\n","      <td>0.826190</td>\n","      <td>0.820476</td>\n","      <td>0.815714</td>\n","      <td>0.814762</td>\n","      <td>0.812381</td>\n","      <td>0.808571</td>\n","      <td>0.820476</td>\n","      <td>0.819143</td>\n","      <td>0.005702</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>5.637032</td>\n","      <td>0.040192</td>\n","      <td>0.019834</td>\n","      <td>0.000318</td>\n","      <td>14</td>\n","      <td>{'max_features': 14}</td>\n","      <td>0.823333</td>\n","      <td>0.821429</td>\n","      <td>0.824286</td>\n","      <td>0.825238</td>\n","      <td>0.817619</td>\n","      <td>0.815238</td>\n","      <td>0.815714</td>\n","      <td>0.813333</td>\n","      <td>0.810952</td>\n","      <td>0.820000</td>\n","      <td>0.818714</td>\n","      <td>0.004642</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>7.870207</td>\n","      <td>0.037199</td>\n","      <td>0.019700</td>\n","      <td>0.000198</td>\n","      <td>20</td>\n","      <td>{'max_features': 20}</td>\n","      <td>0.824286</td>\n","      <td>0.821905</td>\n","      <td>0.823810</td>\n","      <td>0.826667</td>\n","      <td>0.817143</td>\n","      <td>0.815714</td>\n","      <td>0.814286</td>\n","      <td>0.815238</td>\n","      <td>0.809048</td>\n","      <td>0.820952</td>\n","      <td>0.818905</td>\n","      <td>0.005212</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>9.365761</td>\n","      <td>0.030435</td>\n","      <td>0.019946</td>\n","      <td>0.000579</td>\n","      <td>24</td>\n","      <td>{'max_features': 24}</td>\n","      <td>0.825238</td>\n","      <td>0.823333</td>\n","      <td>0.824762</td>\n","      <td>0.826190</td>\n","      <td>0.816667</td>\n","      <td>0.817143</td>\n","      <td>0.815238</td>\n","      <td>0.813810</td>\n","      <td>0.810000</td>\n","      <td>0.820000</td>\n","      <td>0.819238</td>\n","      <td>0.005235</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score\n","0       1.846149      0.020484  ...        0.005692                5\n","1       3.335710      0.014387  ...        0.005702                2\n","2       5.637032      0.040192  ...        0.004642                4\n","3       7.870207      0.037199  ...        0.005212                3\n","4       9.365761      0.030435  ...        0.005235                1\n","\n","[5 rows x 19 columns]"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"code","metadata":{"id":"7dubWkgpwGHr","colab_type":"code","outputId":"f84b1ed3-a185-4290-c22d-14c02be7100b","executionInfo":{"status":"ok","timestamp":1587345764453,"user_tz":300,"elapsed":927827,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["# Plot test accuracies with max_depth.\n","plt.figure(figsize=(6,4))\n","plt.plot(cv_results[\"param_max_features\"], cv_results[\"mean_test_score\"], label=\"Test accuracy\", linewidth=2.5)\n","plt.xlabel(\"max_features\", fontsize=16)\n","plt.ylabel(\"Accuracy\", fontsize=16)\n","plt.legend()\n","plt.show()"],"execution_count":21,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"peu5aLM5w4Oq","colab_type":"code","outputId":"ebfe154c-8acd-41f6-95c1-9745faaed84f","executionInfo":{"status":"ok","timestamp":1587345899374,"user_tz":300,"elapsed":1062716,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Create a KFold object with 10 splits.\n","%%time\n","n_folds = 10\n","\n","# Specify range of hyperparameters.\n","# Set the parameters by cross-validation\n","hyper_params = {'min_samples_leaf': range(100, 400, 50)}\n","\n","# Random Forest model\n","rfc = RandomForestClassifier()\n","\n","# Set up GridSearchCV() funcion.\n","rfc = GridSearchCV(rfc, hyper_params, cv=n_folds, scoring=\"accuracy\")\n","\n","# Fit the model.\n","rfc.fit(X_train, y_train)"],"execution_count":22,"outputs":[{"output_type":"stream","text":["CPU times: user 2min 13s, sys: 52 ms, total: 2min 13s\n","Wall time: 2min 13s\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"W22uJ9GTxB4i","colab_type":"code","outputId":"d2f2e981-672c-4ff2-b74e-5a1624b3d40b","executionInfo":{"status":"ok","timestamp":1587345899375,"user_tz":300,"elapsed":1062697,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":357}},"source":["# Cross-Validation results.\n","cv_results = pd.DataFrame(rfc.cv_results_)\n","cv_results"],"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>mean_fit_time</th>\n","      <th>std_fit_time</th>\n","      <th>mean_score_time</th>\n","      <th>std_score_time</th>\n","      <th>param_min_samples_leaf</th>\n","      <th>params</th>\n","      <th>split0_test_score</th>\n","      <th>split1_test_score</th>\n","      <th>split2_test_score</th>\n","      <th>split3_test_score</th>\n","      <th>split4_test_score</th>\n","      <th>split5_test_score</th>\n","      <th>split6_test_score</th>\n","      <th>split7_test_score</th>\n","      <th>split8_test_score</th>\n","      <th>split9_test_score</th>\n","      <th>mean_test_score</th>\n","      <th>std_test_score</th>\n","      <th>rank_test_score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2.556059</td>\n","      <td>0.032019</td>\n","      <td>0.026794</td>\n","      <td>0.000294</td>\n","      <td>100</td>\n","      <td>{'min_samples_leaf': 100}</td>\n","      <td>0.819524</td>\n","      <td>0.820952</td>\n","      <td>0.825714</td>\n","      <td>0.815714</td>\n","      <td>0.820000</td>\n","      <td>0.811905</td>\n","      <td>0.813333</td>\n","      <td>0.812857</td>\n","      <td>0.806190</td>\n","      <td>0.820476</td>\n","      <td>0.816667</td>\n","      <td>0.005413</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.337767</td>\n","      <td>0.030867</td>\n","      <td>0.025820</td>\n","      <td>0.001892</td>\n","      <td>150</td>\n","      <td>{'min_samples_leaf': 150}</td>\n","      <td>0.821429</td>\n","      <td>0.815714</td>\n","      <td>0.813810</td>\n","      <td>0.810476</td>\n","      <td>0.814762</td>\n","      <td>0.808095</td>\n","      <td>0.811429</td>\n","      <td>0.799524</td>\n","      <td>0.804286</td>\n","      <td>0.816190</td>\n","      <td>0.811571</td>\n","      <td>0.006020</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2.179288</td>\n","      <td>0.027959</td>\n","      <td>0.024146</td>\n","      <td>0.000362</td>\n","      <td>200</td>\n","      <td>{'min_samples_leaf': 200}</td>\n","      <td>0.819524</td>\n","      <td>0.815714</td>\n","      <td>0.813333</td>\n","      <td>0.810952</td>\n","      <td>0.812857</td>\n","      <td>0.809524</td>\n","      <td>0.806190</td>\n","      <td>0.803333</td>\n","      <td>0.803810</td>\n","      <td>0.815714</td>\n","      <td>0.811095</td>\n","      <td>0.005116</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2.046273</td>\n","      <td>0.018214</td>\n","      <td>0.023007</td>\n","      <td>0.000207</td>\n","      <td>250</td>\n","      <td>{'min_samples_leaf': 250}</td>\n","      <td>0.818095</td>\n","      <td>0.813810</td>\n","      <td>0.811905</td>\n","      <td>0.810476</td>\n","      <td>0.813810</td>\n","      <td>0.802857</td>\n","      <td>0.809524</td>\n","      <td>0.802857</td>\n","      <td>0.803810</td>\n","      <td>0.812857</td>\n","      <td>0.810000</td>\n","      <td>0.004972</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.948252</td>\n","      <td>0.025143</td>\n","      <td>0.022562</td>\n","      <td>0.000245</td>\n","      <td>300</td>\n","      <td>{'min_samples_leaf': 300}</td>\n","      <td>0.812857</td>\n","      <td>0.813810</td>\n","      <td>0.811429</td>\n","      <td>0.810476</td>\n","      <td>0.810000</td>\n","      <td>0.803810</td>\n","      <td>0.805714</td>\n","      <td>0.802381</td>\n","      <td>0.803333</td>\n","      <td>0.811429</td>\n","      <td>0.808524</td>\n","      <td>0.004054</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1.872071</td>\n","      <td>0.015552</td>\n","      <td>0.022684</td>\n","      <td>0.001500</td>\n","      <td>350</td>\n","      <td>{'min_samples_leaf': 350}</td>\n","      <td>0.816667</td>\n","      <td>0.811429</td>\n","      <td>0.810476</td>\n","      <td>0.808095</td>\n","      <td>0.811429</td>\n","      <td>0.799048</td>\n","      <td>0.804286</td>\n","      <td>0.797619</td>\n","      <td>0.803333</td>\n","      <td>0.807143</td>\n","      <td>0.806952</td>\n","      <td>0.005639</td>\n","      <td>6</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score\n","0       2.556059      0.032019  ...        0.005413                1\n","1       2.337767      0.030867  ...        0.006020                2\n","2       2.179288      0.027959  ...        0.005116                3\n","3       2.046273      0.018214  ...        0.004972                4\n","4       1.948252      0.025143  ...        0.004054                5\n","5       1.872071      0.015552  ...        0.005639                6\n","\n","[6 rows x 19 columns]"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"id":"XHC9GBfoxFCr","colab_type":"code","outputId":"4504bb82-3b5c-43f6-fad9-240068f3814c","executionInfo":{"status":"ok","timestamp":1587345899376,"user_tz":300,"elapsed":1062679,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["# Plot test accuracies with max_depth.\n","plt.figure(figsize=(6,4))\n","plt.plot(cv_results[\"param_min_samples_leaf\"], cv_results[\"mean_test_score\"], label=\"Test accuracy\", linewidth=2.5)\n","plt.xlabel(\"max_features\", fontsize=16)\n","plt.ylabel(\"Accuracy\", fontsize=16)\n","plt.legend()\n","plt.show()"],"execution_count":24,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"xCCHNoXTxNMd","colab_type":"code","outputId":"aed642be-6f05-432c-ee64-d3fd7aede8aa","executionInfo":{"status":"ok","timestamp":1587346093908,"user_tz":300,"elapsed":1257189,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Create a KFold object with 10 splits.\n","%%time\n","n_folds = 10\n","\n","# Specify range of hyperparameters.\n","# Set the parameters by cross-validation\n","hyper_params = {'min_samples_split': range(200, 500, 50)}\n","\n","# Random Forest model\n","rfc = RandomForestClassifier()\n","\n","# Set up GridSearchCV() funcion.\n","rfc = GridSearchCV(rfc, hyper_params, cv=n_folds, scoring=\"accuracy\")\n","\n","# Fit the model.\n","rfc.fit(X_train, y_train)"],"execution_count":25,"outputs":[{"output_type":"stream","text":["CPU times: user 3min 15s, sys: 49 ms, total: 3min 15s\n","Wall time: 3min 15s\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"vKYYPju9xRGZ","colab_type":"code","outputId":"29aeb1ff-2bed-477d-cf6a-5d6fbd38aed4","executionInfo":{"status":"ok","timestamp":1587346093913,"user_tz":300,"elapsed":1257172,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":357}},"source":["# Cross-Validation results.\n","cv_results = pd.DataFrame(rfc.cv_results_)\n","cv_results"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>mean_fit_time</th>\n","      <th>std_fit_time</th>\n","      <th>mean_score_time</th>\n","      <th>std_score_time</th>\n","      <th>param_min_samples_split</th>\n","      <th>params</th>\n","      <th>split0_test_score</th>\n","      <th>split1_test_score</th>\n","      <th>split2_test_score</th>\n","      <th>split3_test_score</th>\n","      <th>split4_test_score</th>\n","      <th>split5_test_score</th>\n","      <th>split6_test_score</th>\n","      <th>split7_test_score</th>\n","      <th>split8_test_score</th>\n","      <th>split9_test_score</th>\n","      <th>mean_test_score</th>\n","      <th>std_test_score</th>\n","      <th>rank_test_score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>3.442100</td>\n","      <td>0.025927</td>\n","      <td>0.030719</td>\n","      <td>0.001393</td>\n","      <td>200</td>\n","      <td>{'min_samples_split': 200}</td>\n","      <td>0.823810</td>\n","      <td>0.823810</td>\n","      <td>0.828095</td>\n","      <td>0.824762</td>\n","      <td>0.822381</td>\n","      <td>0.813810</td>\n","      <td>0.816190</td>\n","      <td>0.812857</td>\n","      <td>0.810952</td>\n","      <td>0.821429</td>\n","      <td>0.819810</td>\n","      <td>0.005570</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>3.282255</td>\n","      <td>0.030304</td>\n","      <td>0.029026</td>\n","      <td>0.000523</td>\n","      <td>250</td>\n","      <td>{'min_samples_split': 250}</td>\n","      <td>0.821429</td>\n","      <td>0.821429</td>\n","      <td>0.827143</td>\n","      <td>0.824762</td>\n","      <td>0.819048</td>\n","      <td>0.815238</td>\n","      <td>0.816190</td>\n","      <td>0.813810</td>\n","      <td>0.810000</td>\n","      <td>0.822381</td>\n","      <td>0.819143</td>\n","      <td>0.005025</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.173257</td>\n","      <td>0.046523</td>\n","      <td>0.027923</td>\n","      <td>0.000569</td>\n","      <td>300</td>\n","      <td>{'min_samples_split': 300}</td>\n","      <td>0.821429</td>\n","      <td>0.824762</td>\n","      <td>0.827619</td>\n","      <td>0.822381</td>\n","      <td>0.820476</td>\n","      <td>0.814286</td>\n","      <td>0.814286</td>\n","      <td>0.813333</td>\n","      <td>0.811429</td>\n","      <td>0.820952</td>\n","      <td>0.819095</td>\n","      <td>0.005144</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3.087173</td>\n","      <td>0.035492</td>\n","      <td>0.027657</td>\n","      <td>0.001533</td>\n","      <td>350</td>\n","      <td>{'min_samples_split': 350}</td>\n","      <td>0.823333</td>\n","      <td>0.822857</td>\n","      <td>0.824762</td>\n","      <td>0.825238</td>\n","      <td>0.820000</td>\n","      <td>0.816190</td>\n","      <td>0.815238</td>\n","      <td>0.812857</td>\n","      <td>0.811429</td>\n","      <td>0.818095</td>\n","      <td>0.819000</td>\n","      <td>0.004740</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>3.084661</td>\n","      <td>0.079770</td>\n","      <td>0.026879</td>\n","      <td>0.001245</td>\n","      <td>400</td>\n","      <td>{'min_samples_split': 400}</td>\n","      <td>0.821429</td>\n","      <td>0.822857</td>\n","      <td>0.825238</td>\n","      <td>0.824762</td>\n","      <td>0.820000</td>\n","      <td>0.815714</td>\n","      <td>0.814286</td>\n","      <td>0.812857</td>\n","      <td>0.809048</td>\n","      <td>0.821429</td>\n","      <td>0.818762</td>\n","      <td>0.005187</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>2.906227</td>\n","      <td>0.023807</td>\n","      <td>0.026775</td>\n","      <td>0.002460</td>\n","      <td>450</td>\n","      <td>{'min_samples_split': 450}</td>\n","      <td>0.822381</td>\n","      <td>0.825714</td>\n","      <td>0.826667</td>\n","      <td>0.823333</td>\n","      <td>0.819524</td>\n","      <td>0.813333</td>\n","      <td>0.814286</td>\n","      <td>0.812381</td>\n","      <td>0.811905</td>\n","      <td>0.820000</td>\n","      <td>0.818952</td>\n","      <td>0.005327</td>\n","      <td>5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score\n","0       3.442100      0.025927  ...        0.005570                1\n","1       3.282255      0.030304  ...        0.005025                2\n","2       3.173257      0.046523  ...        0.005144                3\n","3       3.087173      0.035492  ...        0.004740                4\n","4       3.084661      0.079770  ...        0.005187                6\n","5       2.906227      0.023807  ...        0.005327                5\n","\n","[6 rows x 19 columns]"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"code","metadata":{"id":"6X1RteDnxUdw","colab_type":"code","outputId":"25b95e3a-98b8-4d1c-bea5-cb1848894b83","executionInfo":{"status":"ok","timestamp":1587346093916,"user_tz":300,"elapsed":1257154,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["# Plot test accuracies with max_depth.\n","plt.figure(figsize=(6,4))\n","plt.plot(cv_results[\"param_min_samples_split\"], cv_results[\"mean_test_score\"], label=\"Test accuracy\", linewidth=2.5)\n","plt.xlabel(\"max_features\", fontsize=16)\n","plt.ylabel(\"Accuracy\", fontsize=16)\n","plt.legend()\n","plt.show()"],"execution_count":27,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"iSMmwsGOxpYD","colab_type":"text"},"source":["### **Hyperparameter Tuning using Grid Search**"]},{"cell_type":"code","metadata":{"id":"VIQfTb7Fxp9P","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":51},"outputId":"0446edf4-db4c-4d9a-d51c-b1ee8b452c97","executionInfo":{"status":"ok","timestamp":1587350550675,"user_tz":300,"elapsed":2634122,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}}},"source":["# Create a KFold object with 10 splits.\n","%%time\n","n_folds = 10\n","\n","# Specify range of hyperparameters.\n","# Set the parameters by cross-validation\n","param_grid = {\n","    'max_depth': [4,8,10],\n","    'min_samples_leaf': range(100, 400, 200),\n","    'min_samples_split': range(200, 500, 200),\n","    'n_estimators': [100,200, 300], \n","    'max_features': [5, 10]\n","}\n","\n","# Random Forest model\n","rfc = RandomForestClassifier()\n","\n","# Set up GridSearchCV() funcion.\n","grid_search = GridSearchCV(rfc, param_grid, cv=n_folds, scoring=\"accuracy\")\n","\n","# Fit the model.\n","grid_search.fit(X_train, y_train)"],"execution_count":28,"outputs":[{"output_type":"stream","text":["CPU times: user 1h 14min 13s, sys: 1.24 s, total: 1h 14min 14s\n","Wall time: 1h 14min 16s\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"d5q_APWwyCfH","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"16af7587-45ea-4f39-ddcd-70da26020d05","executionInfo":{"status":"ok","timestamp":1587350550677,"user_tz":300,"elapsed":55,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}}},"source":["# Print the optimal accuracy score and hyperparameters.\n","print('Optimal accuracy:', grid_search.best_score_, 'using', grid_search.best_params_)"],"execution_count":29,"outputs":[{"output_type":"stream","text":["Optimal accuracy: 0.8189523809523809 using {'max_depth': 4, 'max_features': 10, 'min_samples_leaf': 100, 'min_samples_split': 200, 'n_estimators': 300}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"8qgE41J-ySNO","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":51},"outputId":"a5d7c0ec-ff5a-43e3-a669-1ecba8ac3e4b","executionInfo":{"status":"ok","timestamp":1587350556373,"user_tz":300,"elapsed":5711,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}}},"source":["# Randome Forest model with the optimal hyperparameters.\n","%%time\n","rfc = RandomForestClassifier(bootstrap=True,\n","                             max_depth=10,\n","                             min_samples_leaf=100, \n","                             min_samples_split=200,\n","                             max_features=10,\n","                             n_estimators=100)\n","# Fit the model.\n","rfc.fit(X_train, y_train)"],"execution_count":30,"outputs":[{"output_type":"stream","text":["CPU times: user 6.07 s, sys: 999 µs, total: 6.07 s\n","Wall time: 6.07 s\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"wnVTgr4Qyls-","colab_type":"code","colab":{}},"source":["# Make predictions.\n","y_pred = rfc.predict(X_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DMG2cJj4yrP3","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"bbb75229-19fa-4ab2-97ac-6400c165204f","executionInfo":{"status":"ok","timestamp":1587350556376,"user_tz":300,"elapsed":28,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}}},"source":["# Print classiffication report.\n","print(classification_report(y_test, y_pred))"],"execution_count":32,"outputs":[{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.84      0.95      0.89      7060\n","           1       0.67      0.36      0.47      1940\n","\n","    accuracy                           0.82      9000\n","   macro avg       0.76      0.66      0.68      9000\n","weighted avg       0.81      0.82      0.80      9000\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"7BAtUEuBy2xH","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":500},"outputId":"6230d914-b044-4c87-e1d6-285e0e2453ca","executionInfo":{"status":"ok","timestamp":1587350556718,"user_tz":300,"elapsed":351,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}}},"source":["# Plot the confusion matrix.\n","%%time\n","cm = metrics.confusion_matrix(y_test, y_pred)\n","plot_confusion_matrix(conf_mat=cm,\n","                      show_absolute=True,\n","                      show_normed=True,\n","                      colorbar=True,\n","                      figsize=(8,8))\n","plt.show()"],"execution_count":33,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x576 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["CPU times: user 162 ms, sys: 4 ms, total: 166 ms\n","Wall time: 166 ms\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"MxxoI4GAy-Nv","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":51},"outputId":"a77a9a13-64ae-4e27-c5d1-6aea41740cbe","executionInfo":{"status":"ok","timestamp":1587350556720,"user_tz":300,"elapsed":37,"user":{"displayName":"Junmo An","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj5i3lgizGZ18Gjf053phbgFazmHzZTmG3QNyhO=s64","userId":"01963327505044644290"}}},"source":["# Print accuracy.\n","print(\"Accuracy:\", metrics.accuracy_score(y_test, y_pred), \"\\n\")"],"execution_count":34,"outputs":[{"output_type":"stream","text":["Accuracy: 0.8246666666666667 \n","\n"],"name":"stdout"}]}]}