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from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_classification X, y = make_classification(n_samples=1000, n_features=4, n_informative=2, n_redundant=0, random_state=0, shuffle=False) clf = RandomForestClassifier(max_d

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kodai answered on July 5, 2022 Popularity 1/10 Helpfulness 3/10

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  • answer from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_classification X, y = make_classification(n_samples=1000, n_features=4, n_informative=2, n_redundant=0, random_state=0, shuffle=False) clf = RandomForestClassifier(max_d

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  • import RandomForestClassifier
  • randomforestregressor in sklearn
  • model = DecisionTreeClassifier(random_state=1) model.fit(X_train, y_train)
  • >>> from sklearn.neural_network import MLPClassifier >>> from sklearn.datasets import make_classification >>> from sklearn.model_selection import train_test_split >>> X, y = make_classification(n_samples=100, random_state=1) >>> X_train, X_test, y_train,

    • from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_classification X, y = make_classification(n_samples=1000, n_features=4, n_informative=2, n_redundant=0, random_state=0, shuffle=False) clf = RandomForestClassifier(max_d

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    Popularity 1/10 Helpfulness 3/10 Language typescript
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    kodai
    Contributed on Jul 05 2022
    kodai
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