python-scikit-learnLogistic regression feature importance

Feature importance for logistic regression can be roughly estimated by using trained model coefficients (make sure all features have the same scale or are normalized):

from sklearn import datasets, linear_model, 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 = linear_model.LogisticRegression()
model.fit(X_train, y_train)

for f, im in enumerate(model.coef_[0]):
  print('Feature', (f+1), 'importance is', im)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	`.LogisticRegression()` creates logistics regression model
`.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

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 = linear_model.LogisticRegression()
model.fit(X_train, y_train)

for f, im in enumerate(model.coef_[0]):
  print('Feature', (f+1), 'importance is', im)

output

Feature 1 importance is -0.4410527873205119
Feature 2 importance is 0.9142380692659069
Feature 3 importance is -2.3368916600655116
Feature 4 importance is -0.996933531643161

More of Python Scikit Learn

See more codes...

python-scikit-learnLogistic regression feature importance

Usage example

output

Related

More of Python Scikit Learn