Recipe: How to use RandomForest Classifier and Regressor in Python?
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How to use RandomForest Classifier and Regressor in Python?

This recipe helps you use RandomForest Classifier and Regressor in Python
In [2]:
## How to use RandomForest Classifier and Regressor in Python
def Snippet_163():
    print()
    print(format('How to use RandomForest Classifier and Regressor in Python','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from sklearn import datasets
    from sklearn import metrics
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.ensemble import RandomForestRegressor
    from sklearn.model_selection import train_test_split
    import matplotlib.pyplot as plt
    import seaborn as sns

    plt.style.use('ggplot')

    # load the iris datasets
    dataset = datasets.load_iris()
    X = dataset.data; y = dataset.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

    # fit a CART model to the data
    model = RandomForestClassifier()
    model.fit(X_train, y_train)
    print(); print(model)

    # make predictions
    expected_y  = y_test
    predicted_y = model.predict(X_test)

    # summarize the fit of the model
    print(); print(metrics.classification_report(expected_y, predicted_y))
    print(); print(metrics.confusion_matrix(expected_y, predicted_y))

    # load the boston datasets
    dataset = datasets.load_boston()
    X = dataset.data; y = dataset.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

    # fit a CART model to the data
    model = RandomForestRegressor()
    model.fit(X_train, y_train)
    print(); print(model)

    # make predictions
    expected_y  = y_test
    predicted_y = model.predict(X_test)

    # summarize the fit of the model
    print(); print(metrics.r2_score(expected_y, predicted_y))
    print(); print(metrics.mean_squared_log_error(expected_y, predicted_y))

    # plot regression
    plt.figure(figsize=(10,10))
    sns.regplot(expected_y, predicted_y, fit_reg=True, scatter_kws={"s": 100})

Snippet_163()
************How to use RandomForest Classifier and Regressor in Python************

RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,
            oob_score=False, random_state=None, verbose=0,
            warm_start=False)

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        14
           1       0.88      0.88      0.88         8
           2       0.94      0.94      0.94        16

   micro avg       0.95      0.95      0.95        38
   macro avg       0.94      0.94      0.94        38
weighted avg       0.95      0.95      0.95        38


[[14  0  0]
 [ 0  7  1]
 [ 0  1 15]]

RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
           max_features='auto', max_leaf_nodes=None,
           min_impurity_decrease=0.0, min_impurity_split=None,
           min_samples_leaf=1, min_samples_split=2,
           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,
           oob_score=False, random_state=None, verbose=0, warm_start=False)

0.8730809570512008

0.02582975545774084


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