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 + cgithub

`from sklearn import` import module from scikit-learn	`load_iris` loads Iris dataset
`model_selection.train_test_split` splits given `X` and `y` datasets to test (25% of values by default) and train (75% of values by default) subsets	`.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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python-scikit-learnFeature importance example

Usage example

output

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