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I'm working on this dataset. I'm trying to select features using Random Forest. This is the relevant code:

x_train, y_train, x_test, y_test = train_test_split(train, test_size = 0.3)

sel = SelectFromModel(RandomForestClassifier(n_estimators=100))
sel.fit(x_train, y_train)
sel.get_support()
selected_feat = x_train.columns[sel.get_support()]

The code x_train, y_train, x_test, y_test = train_test_split(train, test_size = 0.3) throws the following ValueError:

Traceback (most recent call last):
  File "C:/Users/security/Downloads/AP/Boston-Kaggle/Boston.py", line 25, in <module>
    x_train, y_train, x_test, y_test = train_test_split(train, test_size = 0.3)
ValueError: not enough values to unpack (expected 4, got 2)

I don't know what it means by not enough values. The dataset has a big dimension.

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1 Answer 1

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This is the implementation of the train_test_split function 

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

Split your train into X and y and try again!

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  • $\begingroup$ What are the parameters X and y before test_size? $\endgroup$
    – Andros Adrianopolos
    Jul 19, 2019 at 9:51
  • $\begingroup$ It's your train splitted in 2, X are all the features, y is your target variable. Separate the features from the target and apply the train_test_split $\endgroup$
    – Blenz
    Jul 19, 2019 at 9:52

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