python-scikit-learnLinear model feature importance calculation
For linear models, trained coefficients can be used as feature importance values:
from sklearn import datasets, linear_model, model_selection, metrics
X, y = datasets.load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = linear_model.LinearRegression()
model.fit(X_train, y_train)
fi = model.coef_ctrl + cfrom sklearn importimport module from scikit-learn | datasets.load_diabetesloads sample diabetes database |
model_selection.train_test_splitsplits given | .fit(train model with a given features and target variable dataset |
.coef_returns list of coefficients of a trained model | |
Usage example
from sklearn import datasets, linear_model, model_selection, metrics
X, y = datasets.load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = linear_model.LinearRegression()
model.fit(X_train, y_train)
fi = model.coef_
print(fi)output
[ 7.4644251 -212.29645468 484.41681905 275.862333 -938.22675656
587.40072837 114.72619725 120.06905393 872.88971664 45.45492861]
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