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model = DecisionTreeClassifier(random_state=1) model.fit(X_train, y_train)

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Evil Eel answered on August 26, 2022 Popularity 1/10 Helpfulness 1/10

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  • answer model = DecisionTreeClassifier(random_state=1) model.fit(X_train, y_train)

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  • decisiontreeclassifier sklearn
  • 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
  • >>> 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,

    • model = DecisionTreeClassifier(random_state=1) model.fit(X_train, y_train)

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    Popularity 1/10 Helpfulness 1/10 Language python
    Source: olympus1.mygreatlearning.com
    Tags: model python
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    Evil Eel
    Contributed on Aug 26 2022
    Evil Eel
    0 Answers  Avg Quality 2/10

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