python-scikit-learnFeature importance example
For most models, feature_importances_ model attribute is available after training:
from sklearn import datasets, ensemble, model_selection
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = ensemble.RandomForestClassifier()
model.fit(X_train, y_train)
fi = model.feature_importances_ctrl + cfrom sklearn importimport module from scikit-learn | load_irisloads Iris dataset |
model_selection.train_test_splitsplits given | .RandomForestClassifier(creates random forest classification model |
.fit(train model with a given features and target variable dataset | .feature_importances_returns trained model feature importance list |
Usage example
from sklearn import datasets, ensemble, model_selection
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = ensemble.RandomForestClassifier()
model.fit(X_train, y_train)
fi = model.feature_importances_
print(fi)output
[0.11663411 0.03627566 0.39945207 0.44763817]
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