ensemble-ml-algorithms-bagging-boosting-voting.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/faressayah/ensemble-ml-algorithms-bagging-boosting-voting?scriptVersionId=117870817\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"attachments":{"0_yMbDVA-mPWvzFXCM.png":{"image/png":"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"}},"cell_type":"markdown","id":"b7121993","metadata":{"papermill":{"duration":0.012859,"end_time":"2023-01-31T22:08:15.334526","exception":false,"start_time":"2023-01-31T22:08:15.321667","status":"completed"},"tags":[]},"source":["# Ensemble Machine Learning Algorithms in Python with scikit-learn\n","\n","> Ensembles can give you a boost in accuracy on your dataset.\n","![0_yMbDVA-mPWvzFXCM.png](attachment:0_yMbDVA-mPWvzFXCM.png)\n","\n","> In this notebook you will discover how you can create some of the most powerful types of ensembles in Python using Scikit-Learn.\n","\n","# Combine Model Predictions Into Ensemble Predictions\n","\n","The three most popular methods for combining the predictions from different models are:\n","\n","- **Bagging**. Building multiple models (typically of the same type) from different subsamples of the training dataset.\n","- **Boosting**. Building multiple models (typically of the same type) each of which learns to fix the prediction errors of a prior model in the chain.\n","- **Voting**. Building multiple models (typically of differing types) and simple statistics (like calculating the mean) are used to combine predictions.\n","\n","***\n","\n","A standard classification problem used to demonstrate each ensemble algorithm is the Pima Indian's onset of diabetes dataset. It is a binary classification problem where all of the input variables are numeric and have differing scales.\n","***"]},{"cell_type":"markdown","id":"d444535f","metadata":{"papermill":{"duration":0.01058,"end_time":"2023-01-31T22:08:15.356308","exception":false,"start_time":"2023-01-31T22:08:15.345728","status":"completed"},"tags":[]},"source":["## Importing the libraries"]},{"cell_type":"code","execution_count":1,"id":"c9448b34","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:15.38123Z","iopub.status.busy":"2023-01-31T22:08:15.380541Z","iopub.status.idle":"2023-01-31T22:08:16.50687Z","shell.execute_reply":"2023-01-31T22:08:16.505696Z"},"papermill":{"duration":1.14256,"end_time":"2023-01-31T22:08:16.5099","exception":false,"start_time":"2023-01-31T22:08:15.36734","status":"completed"},"tags":[]},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","%matplotlib inline\n","sns.set_style(\"whitegrid\")\n","plt.style.use(\"fivethirtyeight\")"]},{"cell_type":"markdown","id":"6465409f","metadata":{"papermill":{"duration":0.010143,"end_time":"2023-01-31T22:08:16.531128","exception":false,"start_time":"2023-01-31T22:08:16.520985","status":"completed"},"tags":[]},"source":["## Loading the data"]},{"cell_type":"code","execution_count":2,"id":"1248c496","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:16.557964Z","iopub.status.busy":"2023-01-31T22:08:16.557545Z","iopub.status.idle":"2023-01-31T22:08:16.602408Z","shell.execute_reply":"2023-01-31T22:08:16.601106Z"},"papermill":{"duration":0.063101,"end_time":"2023-01-31T22:08:16.605656","exception":false,"start_time":"2023-01-31T22:08:16.542555","status":"completed"},"tags":[]},"outputs":[{"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>Pregnancies</th>\n","      <th>Glucose</th>\n","      <th>BloodPressure</th>\n","      <th>SkinThickness</th>\n","      <th>Insulin</th>\n","      <th>BMI</th>\n","      <th>DiabetesPedigreeFunction</th>\n","      <th>Age</th>\n","      <th>Outcome</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>6</td>\n","      <td>148</td>\n","      <td>72</td>\n","      <td>35</td>\n","      <td>0</td>\n","      <td>33.6</td>\n","      <td>0.627</td>\n","      <td>50</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>85</td>\n","      <td>66</td>\n","      <td>29</td>\n","      <td>0</td>\n","      <td>26.6</td>\n","      <td>0.351</td>\n","      <td>31</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>8</td>\n","      <td>183</td>\n","      <td>64</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>23.3</td>\n","      <td>0.672</td>\n","      <td>32</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>89</td>\n","      <td>66</td>\n","      <td>23</td>\n","      <td>94</td>\n","      <td>28.1</td>\n","      <td>0.167</td>\n","      <td>21</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>137</td>\n","      <td>40</td>\n","      <td>35</td>\n","      <td>168</td>\n","      <td>43.1</td>\n","      <td>2.288</td>\n","      <td>33</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n","0            6      148             72             35        0  33.6   \n","1            1       85             66             29        0  26.6   \n","2            8      183             64              0        0  23.3   \n","3            1       89             66             23       94  28.1   \n","4            0      137             40             35      168  43.1   \n","\n","   DiabetesPedigreeFunction  Age  Outcome  \n","0                     0.627   50        1  \n","1                     0.351   31        0  \n","2                     0.672   32        1  \n","3                     0.167   21        0  \n","4                     2.288   33        1  "]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.read_csv(\"../input/pima-indians-diabetes-database/diabetes.csv\")\n","df.head()"]},{"cell_type":"code","execution_count":3,"id":"d50b7275","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:16.634279Z","iopub.status.busy":"2023-01-31T22:08:16.633675Z","iopub.status.idle":"2023-01-31T22:08:16.661075Z","shell.execute_reply":"2023-01-31T22:08:16.659702Z"},"papermill":{"duration":0.047827,"end_time":"2023-01-31T22:08:16.664727","exception":false,"start_time":"2023-01-31T22:08:16.6169","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 768 entries, 0 to 767\n","Data columns (total 9 columns):\n"," #   Column                    Non-Null Count  Dtype  \n","---  ------                    --------------  -----  \n"," 0   Pregnancies               768 non-null    int64  \n"," 1   Glucose                   768 non-null    int64  \n"," 2   BloodPressure             768 non-null    int64  \n"," 3   SkinThickness             768 non-null    int64  \n"," 4   Insulin                   768 non-null    int64  \n"," 5   BMI                       768 non-null    float64\n"," 6   DiabetesPedigreeFunction  768 non-null    float64\n"," 7   Age                       768 non-null    int64  \n"," 8   Outcome                   768 non-null    int64  \n","dtypes: float64(2), int64(7)\n","memory usage: 54.1 KB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":4,"id":"c9c223ed","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:16.690422Z","iopub.status.busy":"2023-01-31T22:08:16.690032Z","iopub.status.idle":"2023-01-31T22:08:16.700089Z","shell.execute_reply":"2023-01-31T22:08:16.698811Z"},"papermill":{"duration":0.025116,"end_time":"2023-01-31T22:08:16.702337","exception":false,"start_time":"2023-01-31T22:08:16.677221","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Pregnancies                 0\n","Glucose                     0\n","BloodPressure               0\n","SkinThickness               0\n","Insulin                     0\n","BMI                         0\n","DiabetesPedigreeFunction    0\n","Age                         0\n","Outcome                     0\n","dtype: int64"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df.isnull().sum()"]},{"cell_type":"code","execution_count":5,"id":"06257485","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:16.727628Z","iopub.status.busy":"2023-01-31T22:08:16.727233Z","iopub.status.idle":"2023-01-31T22:08:16.764387Z","shell.execute_reply":"2023-01-31T22:08:16.763488Z"},"papermill":{"duration":0.052236,"end_time":"2023-01-31T22:08:16.766918","exception":false,"start_time":"2023-01-31T22:08:16.714682","status":"completed"},"tags":[]},"outputs":[{"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>Pregnancies</th>\n","      <th>Glucose</th>\n","      <th>BloodPressure</th>\n","      <th>SkinThickness</th>\n","      <th>Insulin</th>\n","      <th>BMI</th>\n","      <th>DiabetesPedigreeFunction</th>\n","      <th>Age</th>\n","      <th>Outcome</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>768.00</td>\n","      <td>768.00</td>\n","      <td>768.00</td>\n","      <td>768.00</td>\n","      <td>768.00</td>\n","      <td>768.00</td>\n","      <td>768.00</td>\n","      <td>768.00</td>\n","      <td>768.00</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>3.85</td>\n","      <td>120.89</td>\n","      <td>69.11</td>\n","      <td>20.54</td>\n","      <td>79.80</td>\n","      <td>31.99</td>\n","      <td>0.47</td>\n","      <td>33.24</td>\n","      <td>0.35</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>3.37</td>\n","      <td>31.97</td>\n","      <td>19.36</td>\n","      <td>15.95</td>\n","      <td>115.24</td>\n","      <td>7.88</td>\n","      <td>0.33</td>\n","      <td>11.76</td>\n","      <td>0.48</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.08</td>\n","      <td>21.00</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>1.00</td>\n","      <td>99.00</td>\n","      <td>62.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>27.30</td>\n","      <td>0.24</td>\n","      <td>24.00</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>3.00</td>\n","      <td>117.00</td>\n","      <td>72.00</td>\n","      <td>23.00</td>\n","      <td>30.50</td>\n","      <td>32.00</td>\n","      <td>0.37</td>\n","      <td>29.00</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>6.00</td>\n","      <td>140.25</td>\n","      <td>80.00</td>\n","      <td>32.00</td>\n","      <td>127.25</td>\n","      <td>36.60</td>\n","      <td>0.63</td>\n","      <td>41.00</td>\n","      <td>1.00</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>17.00</td>\n","      <td>199.00</td>\n","      <td>122.00</td>\n","      <td>99.00</td>\n","      <td>846.00</td>\n","      <td>67.10</td>\n","      <td>2.42</td>\n","      <td>81.00</td>\n","      <td>1.00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin    BMI  \\\n","count       768.00   768.00         768.00         768.00   768.00 768.00   \n","mean          3.85   120.89          69.11          20.54    79.80  31.99   \n","std           3.37    31.97          19.36          15.95   115.24   7.88   \n","min           0.00     0.00           0.00           0.00     0.00   0.00   \n","25%           1.00    99.00          62.00           0.00     0.00  27.30   \n","50%           3.00   117.00          72.00          23.00    30.50  32.00   \n","75%           6.00   140.25          80.00          32.00   127.25  36.60   \n","max          17.00   199.00         122.00          99.00   846.00  67.10   \n","\n","       DiabetesPedigreeFunction    Age  Outcome  \n","count                    768.00 768.00   768.00  \n","mean                       0.47  33.24     0.35  \n","std                        0.33  11.76     0.48  \n","min                        0.08  21.00     0.00  \n","25%                        0.24  24.00     0.00  \n","50%                        0.37  29.00     0.00  \n","75%                        0.63  41.00     1.00  \n","max                        2.42  81.00     1.00  "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["pd.set_option('display.float_format', '{:.2f}'.format)\n","df.describe()"]},{"cell_type":"code","execution_count":6,"id":"8a62d76d","metadata":{"_kg_hide-output":true,"execution":{"iopub.execute_input":"2023-01-31T22:08:16.792008Z","iopub.status.busy":"2023-01-31T22:08:16.791515Z","iopub.status.idle":"2023-01-31T22:08:16.801935Z","shell.execute_reply":"2023-01-31T22:08:16.80085Z"},"papermill":{"duration":0.025417,"end_time":"2023-01-31T22:08:16.804128","exception":false,"start_time":"2023-01-31T22:08:16.778711","status":"completed"},"tags":[]},"outputs":[],"source":["categorical_val = []\n","continous_val = []\n","for column in df.columns:\n","#     print('==============================')\n","#     print(f\"{column} : {df[column].unique()}\")\n","    if len(df[column].unique()) <= 10:\n","        categorical_val.append(column)\n","    else:\n","        continous_val.append(column)"]},{"cell_type":"markdown","id":"ef0e12be","metadata":{"papermill":{"duration":0.01098,"end_time":"2023-01-31T22:08:16.826383","exception":false,"start_time":"2023-01-31T22:08:16.815403","status":"completed"},"tags":[]},"source":["# Data Pre-Processing"]},{"cell_type":"code","execution_count":7,"id":"b68cfca5","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:16.85212Z","iopub.status.busy":"2023-01-31T22:08:16.851085Z","iopub.status.idle":"2023-01-31T22:08:16.859404Z","shell.execute_reply":"2023-01-31T22:08:16.858221Z"},"papermill":{"duration":0.024251,"end_time":"2023-01-31T22:08:16.862144","exception":false,"start_time":"2023-01-31T22:08:16.837893","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n","       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n","      dtype='object')"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["df.columns"]},{"cell_type":"code","execution_count":8,"id":"265ace8a","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:16.887935Z","iopub.status.busy":"2023-01-31T22:08:16.887471Z","iopub.status.idle":"2023-01-31T22:08:16.899841Z","shell.execute_reply":"2023-01-31T22:08:16.898385Z"},"papermill":{"duration":0.028061,"end_time":"2023-01-31T22:08:16.902123","exception":false,"start_time":"2023-01-31T22:08:16.874062","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["============================================\n","Pregnancies ==> Missing zeros : 111\n","============================================\n","Glucose ==> Missing zeros : 5\n","============================================\n","BloodPressure ==> Missing zeros : 35\n","============================================\n","SkinThickness ==> Missing zeros : 227\n","============================================\n","Insulin ==> Missing zeros : 374\n","============================================\n","BMI ==> Missing zeros : 11\n","============================================\n","DiabetesPedigreeFunction ==> Missing zeros : 0\n","============================================\n","Age ==> Missing zeros : 0\n"]}],"source":["# How many missing zeros are mising in each feature\n","feature_columns = [\n","    'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', \n","    'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'\n","]\n","\n","for column in feature_columns:\n","    print(\"============================================\")\n","    print(f\"{column} ==> Missing zeros : {len(df.loc[df[column] == 0])}\")"]},{"cell_type":"code","execution_count":9,"id":"ae391180","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:16.927846Z","iopub.status.busy":"2023-01-31T22:08:16.927391Z","iopub.status.idle":"2023-01-31T22:08:17.298385Z","shell.execute_reply":"2023-01-31T22:08:17.296537Z"},"papermill":{"duration":0.387449,"end_time":"2023-01-31T22:08:17.301586","exception":false,"start_time":"2023-01-31T22:08:16.914137","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["============================================\n","Pregnancies ==> Missing zeros : 0\n","============================================\n","Glucose ==> Missing zeros : 0\n","============================================\n","BloodPressure ==> Missing zeros : 0\n","============================================\n","SkinThickness ==> Missing zeros : 0\n","============================================\n","Insulin ==> Missing zeros : 0\n","============================================\n","BMI ==> Missing zeros : 0\n","============================================\n","DiabetesPedigreeFunction ==> Missing zeros : 0\n","============================================\n","Age ==> Missing zeros : 0\n"]}],"source":["from sklearn.impute import SimpleImputer\n","\n","\n","fill_values = SimpleImputer(missing_values=0, strategy=\"mean\", copy=False)\n","df[feature_columns] = fill_values.fit_transform(df[feature_columns])\n","\n","for column in feature_columns:\n","    print(\"============================================\")\n","    print(f\"{column} ==> Missing zeros : {len(df.loc[df[column] == 0])}\")"]},{"cell_type":"code","execution_count":10,"id":"70c8559d","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:17.327391Z","iopub.status.busy":"2023-01-31T22:08:17.326982Z","iopub.status.idle":"2023-01-31T22:08:17.337292Z","shell.execute_reply":"2023-01-31T22:08:17.336002Z"},"papermill":{"duration":0.025607,"end_time":"2023-01-31T22:08:17.339549","exception":false,"start_time":"2023-01-31T22:08:17.313942","status":"completed"},"tags":[]},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","\n","\n","X = df[feature_columns]\n","y = df.Outcome\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"]},{"cell_type":"code","execution_count":11,"id":"018e0a65","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:17.36581Z","iopub.status.busy":"2023-01-31T22:08:17.364994Z","iopub.status.idle":"2023-01-31T22:08:17.374174Z","shell.execute_reply":"2023-01-31T22:08:17.372848Z"},"papermill":{"duration":0.024982,"end_time":"2023-01-31T22:08:17.376537","exception":false,"start_time":"2023-01-31T22:08:17.351555","status":"completed"},"tags":[]},"outputs":[],"source":["from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n","\n","\n","def evaluate(model, X_train, X_test, y_train, y_test):\n","    y_test_pred = model.predict(X_test)\n","    y_train_pred = model.predict(X_train)\n","\n","    print(\"TRAINIG RESULTS: \\n===============================\")\n","    clf_report = pd.DataFrame(classification_report(y_train, y_train_pred, output_dict=True))\n","    print(f\"CONFUSION MATRIX:\\n{confusion_matrix(y_train, y_train_pred)}\")\n","    print(f\"ACCURACY SCORE:\\n{accuracy_score(y_train, y_train_pred):.4f}\")\n","    print(f\"CLASSIFICATION REPORT:\\n{clf_report}\")\n","\n","    print(\"TESTING RESULTS: \\n===============================\")\n","    clf_report = pd.DataFrame(classification_report(y_test, y_test_pred, output_dict=True))\n","    print(f\"CONFUSION MATRIX:\\n{confusion_matrix(y_test, y_test_pred)}\")\n","    print(f\"ACCURACY SCORE:\\n{accuracy_score(y_test, y_test_pred):.4f}\")\n","    print(f\"CLASSIFICATION REPORT:\\n{clf_report}\")"]},{"cell_type":"markdown","id":"4f232ccc","metadata":{"papermill":{"duration":0.011344,"end_time":"2023-01-31T22:08:17.399723","exception":false,"start_time":"2023-01-31T22:08:17.388379","status":"completed"},"tags":[]},"source":["# Bagging Algorithms\n","Bootstrap Aggregation or bagging involves taking multiple samples from your training dataset (with replacement) and training a model for each sample.\n","\n","The final output prediction is averaged across the predictions of all of the sub-models.\n","\n","The three bagging models covered in this section are as follows:\n","\n","1. Bagged Decision Trees\n","2. Random Forest\n","3. Extra Trees"]},{"cell_type":"markdown","id":"b053fabe","metadata":{"papermill":{"duration":0.01127,"end_time":"2023-01-31T22:08:17.42267","exception":false,"start_time":"2023-01-31T22:08:17.4114","status":"completed"},"tags":[]},"source":["## 1. Bagged Decision Trees\n","Bagging performs best with algorithms that have high variance. A popular example is decision trees, often constructed without pruning.\n","\n","**BaggingClassifier**:\n","\n","A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregates their individual predictions (either by voting or by averaging) to form a final prediction. Such a meta-estimator can typically be used as a way to reduce the variance of a black-box estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it.\n","\n","This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting. If samples are drawn with replacement, then the method is known as Bagging. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches.\n","\n","**BaggingClassifier Parameters:**\n","- `base_estimator`: The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a decision tree.\n","***\n","- `n_estimators`: The number of base estimators in the ensemble.\n","***\n","- `max_samples`: The number of samples to draw from X to train each base estimator.\n","***\n","- `max_features`: The number of features to draw from X to train each base estimator.\n","***\n","- `bootstrap`: Whether samples are drawn with replacement. If False, sampling without replacement is performed.\n","***\n","- `bootstrap_features`: Whether features are drawn with replacement.\n","***\n","- `oob_score`: Whether to use out-of-bag samples to estimate the generalization error.\n","***\n","- `warm_start`: When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble."]},{"cell_type":"code","execution_count":12,"id":"2e1726da","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:17.448002Z","iopub.status.busy":"2023-01-31T22:08:17.447568Z","iopub.status.idle":"2023-01-31T22:08:22.550387Z","shell.execute_reply":"2023-01-31T22:08:22.548831Z"},"papermill":{"duration":5.11859,"end_time":"2023-01-31T22:08:22.552988","exception":false,"start_time":"2023-01-31T22:08:17.434398","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["TRAINIG RESULTS: \n","===============================\n","CONFUSION MATRIX:\n","[[349   0]\n"," [  0 188]]\n","ACCURACY SCORE:\n","1.0000\n","CLASSIFICATION REPORT:\n","               0      1  accuracy  macro avg  weighted avg\n","precision   1.00   1.00      1.00       1.00          1.00\n","recall      1.00   1.00      1.00       1.00          1.00\n","f1-score    1.00   1.00      1.00       1.00          1.00\n","support   349.00 188.00      1.00     537.00        537.00\n","TESTING RESULTS: \n","===============================\n","CONFUSION MATRIX:\n","[[119  32]\n"," [ 24  56]]\n","ACCURACY SCORE:\n","0.7576\n","CLASSIFICATION REPORT:\n","               0     1  accuracy  macro avg  weighted avg\n","precision   0.83  0.64      0.76       0.73          0.76\n","recall      0.79  0.70      0.76       0.74          0.76\n","f1-score    0.81  0.67      0.76       0.74          0.76\n","support   151.00 80.00      0.76     231.00        231.00\n"]}],"source":["from sklearn.ensemble import BaggingClassifier\n","from sklearn.tree import DecisionTreeClassifier\n","\n","tree = DecisionTreeClassifier()\n","bagging_clf = BaggingClassifier(base_estimator=tree, n_estimators=1500, random_state=42)\n","bagging_clf.fit(X_train, y_train)\n","\n","evaluate(bagging_clf, X_train, X_test, y_train, y_test)"]},{"cell_type":"code","execution_count":13,"id":"b342650f","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:22.579133Z","iopub.status.busy":"2023-01-31T22:08:22.578681Z","iopub.status.idle":"2023-01-31T22:08:23.086268Z","shell.execute_reply":"2023-01-31T22:08:23.084949Z"},"papermill":{"duration":0.523687,"end_time":"2023-01-31T22:08:23.089393","exception":false,"start_time":"2023-01-31T22:08:22.565706","status":"completed"},"tags":[]},"outputs":[],"source":["scores = {\n","    'Bagging Classifier': {\n","        'Train': accuracy_score(y_train, bagging_clf.predict(X_train)),\n","        'Test': accuracy_score(y_test, bagging_clf.predict(X_test)),\n","    },\n","}"]},{"cell_type":"markdown","id":"602e9fa1","metadata":{"papermill":{"duration":0.012373,"end_time":"2023-01-31T22:08:23.114801","exception":false,"start_time":"2023-01-31T22:08:23.102428","status":"completed"},"tags":[]},"source":["## 2. Random Forest\n","\n","A random forest is a meta-estimator that fits several decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.\n","\n","The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if `bootstrap=True` (default).\n","\n","- **Random forest algorithm parameters:**\n","- `n_estimators`: The number of trees in the forest.\n","*** \n","- `criterion`: The function to measure the quality of a split. Supported criteria are \"`gini`\" for the Gini impurity and \"`entropy`\" for the information gain.\n","***\n","- `max_depth`: The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than `min_samples_split` samples.\n","***\n","- `min_samples_split`: The minimum number of samples required to split an internal node.\n","***\n","- `min_samples_leaf`: The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches.  This may have the effect of smoothing the model, especially in regression.\n","***\n","- `min_weight_fraction_leaf`: The minimum weighted fraction of the total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.\n","***\n","- `max_features`: The number of features to consider when looking for the best split.\n","***\n","- `max_leaf_nodes`: Grow a tree with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then an unlimited number of leaf nodes.\n","***\n","- `min_impurity_decrease`: A node will be split if this split induces a decrease of the impurity greater than or equal to this value.\n","***\n","- `min_impurity_split`: Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise, it is a leaf.\n","***\n","- `bootstrap`: Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.\n","***\n","- `oob_score`: Whether to use out-of-bag samples to estimate the generalization accuracy.\n","***\n","- `warm_start`: When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble."]},{"cell_type":"code","execution_count":14,"id":"77b7deec","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:23.141722Z","iopub.status.busy":"2023-01-31T22:08:23.140637Z","iopub.status.idle":"2023-01-31T22:08:25.408144Z","shell.execute_reply":"2023-01-31T22:08:25.406552Z"},"papermill":{"duration":2.283456,"end_time":"2023-01-31T22:08:25.410823","exception":false,"start_time":"2023-01-31T22:08:23.127367","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["TRAINIG RESULTS: \n","===============================\n","CONFUSION MATRIX:\n","[[349   0]\n"," [  0 188]]\n","ACCURACY SCORE:\n","1.0000\n","CLASSIFICATION REPORT:\n","               0      1  accuracy  macro avg  weighted avg\n","precision   1.00   1.00      1.00       1.00          1.00\n","recall      1.00   1.00      1.00       1.00          1.00\n","f1-score    1.00   1.00      1.00       1.00          1.00\n","support   349.00 188.00      1.00     537.00        537.00\n","TESTING RESULTS: \n","===============================\n","CONFUSION MATRIX:\n","[[123  28]\n"," [ 29  51]]\n","ACCURACY SCORE:\n","0.7532\n","CLASSIFICATION REPORT:\n","               0     1  accuracy  macro avg  weighted avg\n","precision   0.81  0.65      0.75       0.73          0.75\n","recall      0.81  0.64      0.75       0.73          0.75\n","f1-score    0.81  0.64      0.75       0.73          0.75\n","support   151.00 80.00      0.75     231.00        231.00\n"]}],"source":["from sklearn.ensemble import RandomForestClassifier\n","\n","rf_clf = RandomForestClassifier(random_state=42, n_estimators=1000)\n","rf_clf.fit(X_train, y_train)\n","evaluate(rf_clf, X_train, X_test, y_train, y_test)"]},{"cell_type":"code","execution_count":15,"id":"3bb813e2","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:25.436044Z","iopub.status.busy":"2023-01-31T22:08:25.4356Z","iopub.status.idle":"2023-01-31T22:08:25.800718Z","shell.execute_reply":"2023-01-31T22:08:25.799241Z"},"papermill":{"duration":0.38067,"end_time":"2023-01-31T22:08:25.803321","exception":false,"start_time":"2023-01-31T22:08:25.422651","status":"completed"},"tags":[]},"outputs":[],"source":["scores['Random Forest'] = {\n","        'Train': accuracy_score(y_train, rf_clf.predict(X_train)),\n","        'Test': accuracy_score(y_test, rf_clf.predict(X_test)),\n","    }"]},{"cell_type":"markdown","id":"370116e2","metadata":{"papermill":{"duration":0.011736,"end_time":"2023-01-31T22:08:25.827573","exception":false,"start_time":"2023-01-31T22:08:25.815837","status":"completed"},"tags":[]},"source":["## 3. Extra Trees\n","Extra Trees are another modification of bagging where random trees are constructed from samples of the training dataset.\n","\n","You can construct an Extra Trees model for classification using the ExtraTreesClassifier class.\n","\n","**ExtraTreeClassifier**:\n","\n","This class implements a meta-estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.\n","\n","**ExtraTreeClassifier Parameters**:\n","- `n_estimators`: The number of trees in the forest.\n","*** \n","- `criterion`: The function to measure the quality of a split. Supported criteria are \"`gini`\" for the Gini impurity and \"`entropy`\" for the information gain.\n","***\n","- `max_depth`: The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than `min_samples_split` samples.\n","***\n","- `min_samples_split`: The minimum number of samples required to split an internal node.\n","***\n","- `min_samples_leaf`: The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches.  This may have the effect of smoothing the model, especially in regression.\n","***\n","- `min_weight_fraction_leaf`: The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.\n","***\n","- `max_features`: The number of features to consider when looking for the best split.\n","***\n","- `max_leaf_nodes`: Grow a tree with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then an unlimited number of leaf nodes.\n","***\n","- `min_impurity_decrease`: A node will be split if this split induces a decrease of the impurity greater than or equal to this value.\n","***\n","- `min_impurity_split`: Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise, it is a leaf.\n","***\n","- `bootstrap`: Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.\n","***\n","- `oob_score`: Whether to use out-of-bag samples to estimate the generalization accuracy.\n","***\n","- `warm_start`: When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble."]},{"cell_type":"code","execution_count":16,"id":"c626d14c","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:25.854256Z","iopub.status.busy":"2023-01-31T22:08:25.853466Z","iopub.status.idle":"2023-01-31T22:08:27.91374Z","shell.execute_reply":"2023-01-31T22:08:27.912566Z"},"papermill":{"duration":2.077058,"end_time":"2023-01-31T22:08:27.917094","exception":false,"start_time":"2023-01-31T22:08:25.840036","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["TRAINIG RESULTS: \n","===============================\n","CONFUSION MATRIX:\n","[[349   0]\n"," [  0 188]]\n","ACCURACY SCORE:\n","1.0000\n","CLASSIFICATION REPORT:\n","               0      1  accuracy  macro avg  weighted avg\n","precision   1.00   1.00      1.00       1.00          1.00\n","recall      1.00   1.00      1.00       1.00          1.00\n","f1-score    1.00   1.00      1.00       1.00          1.00\n","support   349.00 188.00      1.00     537.00        537.00\n","TESTING RESULTS: \n","===============================\n","CONFUSION MATRIX:\n","[[124  27]\n"," [ 25  55]]\n","ACCURACY SCORE:\n","0.7749\n","CLASSIFICATION REPORT:\n","               0     1  accuracy  macro avg  weighted avg\n","precision   0.83  0.67      0.77       0.75          0.78\n","recall      0.82  0.69      0.77       0.75          0.77\n","f1-score    0.83  0.68      0.77       0.75          0.78\n","support   151.00 80.00      0.77     231.00        231.00\n"]}],"source":["from sklearn.ensemble import ExtraTreesClassifier\n","\n","ex_tree_clf = ExtraTreesClassifier(n_estimators=1000, max_features=7, random_state=42)\n","ex_tree_clf.fit(X_train, y_train)\n","evaluate(ex_tree_clf, X_train, X_test, y_train, y_test)"]},{"cell_type":"code","execution_count":17,"id":"7667b9f1","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:27.945412Z","iopub.status.busy":"2023-01-31T22:08:27.944252Z","iopub.status.idle":"2023-01-31T22:08:28.332274Z","shell.execute_reply":"2023-01-31T22:08:28.330869Z"},"papermill":{"duration":0.405009,"end_time":"2023-01-31T22:08:28.335081","exception":false,"start_time":"2023-01-31T22:08:27.930072","status":"completed"},"tags":[]},"outputs":[],"source":["scores['Extra Tree'] = {\n","        'Train': accuracy_score(y_train, ex_tree_clf.predict(X_train)),\n","        'Test': accuracy_score(y_test, ex_tree_clf.predict(X_test)),\n","    }"]},{"cell_type":"markdown","id":"f3860239","metadata":{"papermill":{"duration":0.012126,"end_time":"2023-01-31T22:08:28.359114","exception":false,"start_time":"2023-01-31T22:08:28.346988","status":"completed"},"tags":[]},"source":["# Boosting Algorithms\n","Boosting ensemble algorithms creates a sequence of models that attempt to correct the mistakes of the models before them in the sequence.\n","\n","Once created, the models make predictions that may be weighted by their demonstrated accuracy and the results are combined to create a final output prediction.\n","\n","The two most common boosting ensemble machine learning algorithms are:\n","\n","1. AdaBoost\n","2. Stochastic Gradient Boosting\n","***"]},{"cell_type":"markdown","id":"fe5fc3af","metadata":{"papermill":{"duration":0.011389,"end_time":"2023-01-31T22:08:28.382371","exception":false,"start_time":"2023-01-31T22:08:28.370982","status":"completed"},"tags":[]},"source":["## 1. AdaBoost\n","AdaBoost was perhaps the first successful boosting ensemble algorithm. It generally works by weighting instances in the dataset by how easy or difficult they are to classify, allowing the algorithm to pay less attention to them in the construction of subsequent models.\n","\n","You can construct an AdaBoost model for classification using the AdaBoostClassifier class.\n","\n","**AdaBoostClassifier**:\n","\n","An AdaBoost classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases.\n","\n","**AdaBoostClassifier Params**:\n","- `base_estimator`: The base estimator from which the boosted ensemble is built.\n","***\n","- `n_estimators`: The maximum number of estimators at which boosting is terminated. In case of the perfect fit, the learning procedure is stopped early.\n","***\n","- `learning_rate`: The learning rate shrinks the contribution of each classifier by ``learning_rate``. There is a trade-off between ``learning_rate`` and ``n_estimators``.\n","***\n","- `algorithm`: If 'SAMME.R' then use the SAMME.R real boosting algorithm. ``base_estimator`` must support the calculation of class probabilities. If 'SAMME' then use the SAMME discrete boosting algorithm. The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations."]},{"cell_type":"code","execution_count":18,"id":"bc82f888","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:28.40827Z","iopub.status.busy":"2023-01-31T22:08:28.407878Z","iopub.status.idle":"2023-01-31T22:08:28.50682Z","shell.execute_reply":"2023-01-31T22:08:28.505529Z"},"papermill":{"duration":0.114945,"end_time":"2023-01-31T22:08:28.509475","exception":false,"start_time":"2023-01-31T22:08:28.39453","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["TRAINIG RESULTS: \n","===============================\n","CONFUSION MATRIX:\n","[[310  39]\n"," [ 51 137]]\n","ACCURACY SCORE:\n","0.8324\n","CLASSIFICATION REPORT:\n","               0      1  accuracy  macro avg  weighted avg\n","precision   0.86   0.78      0.83       0.82          0.83\n","recall      0.89   0.73      0.83       0.81          0.83\n","f1-score    0.87   0.75      0.83       0.81          0.83\n","support   349.00 188.00      0.83     537.00        537.00\n","TESTING RESULTS: \n","===============================\n","CONFUSION MATRIX:\n","[[123  28]\n"," [ 27  53]]\n","ACCURACY SCORE:\n","0.7619\n","CLASSIFICATION REPORT:\n","               0     1  accuracy  macro avg  weighted avg\n","precision   0.82  0.65      0.76       0.74          0.76\n","recall      0.81  0.66      0.76       0.74          0.76\n","f1-score    0.82  0.66      0.76       0.74          0.76\n","support   151.00 80.00      0.76     231.00        231.00\n"]}],"source":["from sklearn.ensemble import AdaBoostClassifier\n","\n","ada_boost_clf = AdaBoostClassifier(n_estimators=30)\n","ada_boost_clf.fit(X_train, y_train)\n","evaluate(ada_boost_clf, X_train, X_test, y_train, y_test)"]},{"cell_type":"code","execution_count":19,"id":"0ebf7d12","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:28.535526Z","iopub.status.busy":"2023-01-31T22:08:28.535114Z","iopub.status.idle":"2023-01-31T22:08:28.561354Z","shell.execute_reply":"2023-01-31T22:08:28.560099Z"},"papermill":{"duration":0.042581,"end_time":"2023-01-31T22:08:28.56422","exception":false,"start_time":"2023-01-31T22:08:28.521639","status":"completed"},"tags":[]},"outputs":[],"source":["scores['AdaBoost'] = {\n","        'Train': accuracy_score(y_train, ada_boost_clf.predict(X_train)),\n","        'Test': accuracy_score(y_test, ada_boost_clf.predict(X_test)),\n","    }"]},{"cell_type":"markdown","id":"7e89d6c3","metadata":{"papermill":{"duration":0.011751,"end_time":"2023-01-31T22:08:28.588422","exception":false,"start_time":"2023-01-31T22:08:28.576671","status":"completed"},"tags":[]},"source":["## 2. Stochastic Gradient Boosting\n","Stochastic Gradient Boosting (also called Gradient Boosting Machines) is one of the most sophisticated ensemble techniques. It is also a technique that is proving to be perhaps of the best techniques available for improving performance via ensembles.\n","\n","**GradientBoostingClassifier**:\n","\n","GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage ``n_classes_`` regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced.\n","\n","**GradientBoostingClassifier Parameters**:\n","\n","- `loss`: loss function to be optimized. 'deviance' refers to deviance (= logistic regression) for classification with probabilistic outputs. For loss 'exponential' gradient boosting recovers the AdaBoost algorithm.\n","***\n","- `learning_rate`: learning rate shrinks the contribution of each tree by `learning_rate`. There is a trade-off between learning_rate and n_estimators.\n","***\n","- `n_estimators`: The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance.\n","***\n","- `subsample`: The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators`. Choosing `subsample < 1.0` leads to a reduction of variance and an increase in bias.\n","***\n","- `criterion`: The function to measure the quality of a split. Supported criteria are \"friedman_mse\" for the mean squared error with an improvement score by Friedman, \"`mse`\" for the mean squared error, and \"`mae`\" for the mean absolute error. The default value of \"friedman_mse\" is generally the best as it can provide a better approximation in some cases.\n","***\n","- `min_samples_split`: The minimum number of samples required to split an internal node.\n","***\n","- `min_samples_leaf`: The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches.  This may have the effect of smoothing the model, especially in regression.\n","***\n","- `min_weight_fraction_leaf`: The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.\n","***\n","- `max_depth`: maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables.\n","***\n","- `min_impurity_decrease`: A node will be split if this split induces a decrease of the impurity greater than or equal to this value.\n","***\n","- `min_impurity_split`: Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise, it is a leaf.\n","***\n","- `max_features`: The number of features to consider when looking for the best split.\n","***\n","- `max_leaf_nodes`: Grow trees with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then an unlimited number of leaf nodes.\n","***\n","- `warm_start`: When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just erase the previous solution.\n","***\n","- `validation_fraction`: The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if ``n_iter_no_change`` is set to an integer.\n","***\n","- `n_iter_no_change`: used to decide if early stopping will be used to terminate training when the validation score is not improving. By default, it is set to None to disable early stopping. If set to a number, it will set aside the ``validation_fraction`` size of the training data as validation and terminate training when the validation score is not improving in all of the previous ``n_iter_no_change`` numbers of iterations. The split is stratified.\n","***\n","- `tol`: Tolerance for the early stopping. When the loss is not improving by at least tol for ``n_iter_no_change`` iterations (if set to a number), the training stops.\n","***\n","- `ccp_alpha`: Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen.\n","***"]},{"cell_type":"code","execution_count":20,"id":"d8e0ca05","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:28.614256Z","iopub.status.busy":"2023-01-31T22:08:28.613849Z","iopub.status.idle":"2023-01-31T22:08:28.78459Z","shell.execute_reply":"2023-01-31T22:08:28.78308Z"},"papermill":{"duration":0.187487,"end_time":"2023-01-31T22:08:28.787948","exception":false,"start_time":"2023-01-31T22:08:28.600461","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["TRAINIG RESULTS: \n","===============================\n","CONFUSION MATRIX:\n","[[342   7]\n"," [ 19 169]]\n","ACCURACY SCORE:\n","0.9516\n","CLASSIFICATION REPORT:\n","               0      1  accuracy  macro avg  weighted avg\n","precision   0.95   0.96      0.95       0.95          0.95\n","recall      0.98   0.90      0.95       0.94          0.95\n","f1-score    0.96   0.93      0.95       0.95          0.95\n","support   349.00 188.00      0.95     537.00        537.00\n","TESTING RESULTS: \n","===============================\n","CONFUSION MATRIX:\n","[[116  35]\n"," [ 26  54]]\n","ACCURACY SCORE:\n","0.7359\n","CLASSIFICATION REPORT:\n","               0     1  accuracy  macro avg  weighted avg\n","precision   0.82  0.61      0.74       0.71          0.74\n","recall      0.77  0.68      0.74       0.72          0.74\n","f1-score    0.79  0.64      0.74       0.72          0.74\n","support   151.00 80.00      0.74     231.00        231.00\n"]}],"source":["from sklearn.ensemble import GradientBoostingClassifier\n","\n","grad_boost_clf = GradientBoostingClassifier(n_estimators=100, random_state=42)\n","grad_boost_clf.fit(X_train, y_train)\n","evaluate(grad_boost_clf, X_train, X_test, y_train, y_test)"]},{"cell_type":"code","execution_count":21,"id":"666bb917","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:28.814756Z","iopub.status.busy":"2023-01-31T22:08:28.814358Z","iopub.status.idle":"2023-01-31T22:08:28.826816Z","shell.execute_reply":"2023-01-31T22:08:28.825326Z"},"papermill":{"duration":0.028709,"end_time":"2023-01-31T22:08:28.829458","exception":false,"start_time":"2023-01-31T22:08:28.800749","status":"completed"},"tags":[]},"outputs":[],"source":["scores['Gradient Boosting'] = {\n","        'Train': accuracy_score(y_train, grad_boost_clf.predict(X_train)),\n","        'Test': accuracy_score(y_test, grad_boost_clf.predict(X_test)),\n","    }"]},{"cell_type":"markdown","id":"d3f0044b","metadata":{"papermill":{"duration":0.011808,"end_time":"2023-01-31T22:08:28.854224","exception":false,"start_time":"2023-01-31T22:08:28.842416","status":"completed"},"tags":[]},"source":["# Voting Ensemble\n","\n","Voting is one of the simplest ways of combining predictions from multiple machine learning algorithms.\n","\n","It works by first creating two or more standalone models from your training dataset. A Voting Classifier can then be used to wrap your models and average the predictions of the sub-models when asked to make predictions for new data.\n","\n","The predictions of the sub-models can be weighted, but specifying the weights for classifiers manually or even heuristically is difficult. More advanced methods can learn how to best weight the predictions from submodels, but this is called stacking (stacked generalization) and is currently not provided in Scikit-learn.\n","\n","**VotingClassifier** : \n","- `estimators`: Invoking the ``fit`` method on the ``VotingClassifier`` will fit clones of those original estimators that will be stored in the class attribute ``self.estimators_``.\n","***\n","- `voting`: If 'hard', uses predicted class labels for majority rule voting. Else if 'soft', predict the class label based on the argmax of the sums of the predicted probabilities, which is recommended for an ensemble of well-calibrated classifiers.\n","***"]},{"cell_type":"code","execution_count":22,"id":"cc9eeb76","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:28.881226Z","iopub.status.busy":"2023-01-31T22:08:28.879821Z","iopub.status.idle":"2023-01-31T22:08:28.959618Z","shell.execute_reply":"2023-01-31T22:08:28.958165Z"},"papermill":{"duration":0.09688,"end_time":"2023-01-31T22:08:28.963233","exception":false,"start_time":"2023-01-31T22:08:28.866353","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["TRAINIG RESULTS: \n","===============================\n","CONFUSION MATRIX:\n","[[327  22]\n"," [ 82 106]]\n","ACCURACY SCORE:\n","0.8063\n","CLASSIFICATION REPORT:\n","               0      1  accuracy  macro avg  weighted avg\n","precision   0.80   0.83      0.81       0.81          0.81\n","recall      0.94   0.56      0.81       0.75          0.81\n","f1-score    0.86   0.67      0.81       0.77          0.80\n","support   349.00 188.00      0.81     537.00        537.00\n","TESTING RESULTS: \n","===============================\n","CONFUSION MATRIX:\n","[[129  22]\n"," [ 38  42]]\n","ACCURACY SCORE:\n","0.7403\n","CLASSIFICATION REPORT:\n","               0     1  accuracy  macro avg  weighted avg\n","precision   0.77  0.66      0.74       0.71          0.73\n","recall      0.85  0.53      0.74       0.69          0.74\n","f1-score    0.81  0.58      0.74       0.70          0.73\n","support   151.00 80.00      0.74     231.00        231.00\n"]}],"source":["from sklearn.ensemble import VotingClassifier\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.svm import SVC\n","\n","estimators = []\n","log_reg = LogisticRegression(solver='liblinear')\n","estimators.append(('Logistic', log_reg))\n","\n","tree = DecisionTreeClassifier()\n","estimators.append(('Tree', tree))\n","\n","svm_clf = SVC(gamma='scale')\n","estimators.append(('SVM', svm_clf))\n","\n","voting = VotingClassifier(estimators=estimators)\n","voting.fit(X_train, y_train)\n","\n","evaluate(voting, X_train, X_test, y_train, y_test)"]},{"cell_type":"code","execution_count":23,"id":"5be32103","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:28.990153Z","iopub.status.busy":"2023-01-31T22:08:28.989726Z","iopub.status.idle":"2023-01-31T22:08:29.022479Z","shell.execute_reply":"2023-01-31T22:08:29.02134Z"},"papermill":{"duration":0.049169,"end_time":"2023-01-31T22:08:29.0251","exception":false,"start_time":"2023-01-31T22:08:28.975931","status":"completed"},"tags":[]},"outputs":[],"source":["scores['Voting'] = {\n","        'Train': accuracy_score(y_train, voting.predict(X_train)),\n","        'Test': accuracy_score(y_test, voting.predict(X_test)),\n","    }"]},{"cell_type":"markdown","id":"afba9229","metadata":{"papermill":{"duration":0.012006,"end_time":"2023-01-31T22:08:29.049516","exception":false,"start_time":"2023-01-31T22:08:29.03751","status":"completed"},"tags":[]},"source":["# Model Comparison"]},{"cell_type":"code","execution_count":24,"id":"5db618bd","metadata":{"execution":{"iopub.execute_input":"2023-01-31T22:08:29.076345Z","iopub.status.busy":"2023-01-31T22:08:29.075724Z","iopub.status.idle":"2023-01-31T22:08:29.438682Z","shell.execute_reply":"2023-01-31T22:08:29.43756Z"},"papermill":{"duration":0.379033,"end_time":"2023-01-31T22:08:29.441129","exception":false,"start_time":"2023-01-31T22:08:29.062096","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["<AxesSubplot:>"]},"execution_count":24,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 1080x576 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["scores_df = pd.DataFrame(scores)\n","\n","scores_df.plot(kind='barh', figsize=(15, 8))"]},{"cell_type":"markdown","id":"684c2c9b","metadata":{"papermill":{"duration":0.01271,"end_time":"2023-01-31T22:08:29.467016","exception":false,"start_time":"2023-01-31T22:08:29.454306","status":"completed"},"tags":[]},"source":["## Summary\n","In this notebook, we discovered ensemble machine learning algorithms for improving the performance of models on your problems.\n","\n","You learned about:\n","\n","1. Bagging Ensembles including Bagged Decision Trees, Random Forest, and Extra Trees.\n","2. Boosting Ensembles including AdaBoost and Stochastic Gradient Boosting.\n","3. Voting Ensembles for averaging the predictions for any arbitrary models.\n","\n","## References:\n","- [Bagging and Random Forest Ensemble Algorithms for Machine Learning](https://machinelearningmastery.com/bagging-and-random-forest-ensemble-algorithms-for-machine-learning/)\n","- [Ensemble Machine Learning Algorithms in Python with scikit-learn](https://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/)\n","[Scikit-learn Library](https://scikit-learn.org/stable/)"]},{"cell_type":"code","execution_count":null,"id":"f65cb169","metadata":{"papermill":{"duration":0.012306,"end_time":"2023-01-31T22:08:29.491989","exception":false,"start_time":"2023-01-31T22:08:29.479683","status":"completed"},"tags":[]},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.12"},"papermill":{"default_parameters":{},"duration":23.963558,"end_time":"2023-01-31T22:08:30.328073","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-01-31T22:08:06.364515","version":"2.3.4"}},"nbformat":4,"nbformat_minor":5}