Feature Importance for regression from sklearn . datasets import make _ regression # define dataset X , y = make_regression ( n_samples = 1000 , n_features = 10 , n_informative = 5 , random_state = 1 ) # summarize the dataset print ( X . shape , y . shape ) #linear regression feature importance from sklearn . datasets import make_regression from sklearn . linear_model import LinearRegression from matplotlib import pyplot # define dataset X , y = make_regression ( n_samples = 1000 , n_features = 10 , n_informative = 5 , random_state = 1 ) # define the model model = LinearRegression ( ) # fit the model model . fit ( X , y ) # get importance importance = model . coef _ # summarize feature importance for i , v in enumerate ( importance ) : print ( 'Feature: %0d, Score: %.5f' % ( i , v ) ) # plot feature importance pyplot . bar ( [ x for x in range ( len ( importance ) ) ] , importance ) pyplot . show ( ) feature importance for classification <div&g