varimportance py import numpy as np from sklearn datasets import make_classification from sklearn ensemble import randomforestclassifier import matplotlib pyplot as plt pylab n_points create regression data with data points xy make_classification n_samples =n_points n_features = n_informative = n_redundant = n_repeated = random_state = shuffle false rf randomforestclassifier n_estimators = max_features ="log "rf fit( ,yimportances rf feature_importances_ indices np argsort importances )[:- for in range ( )print (feature % (% )indices [ ]+ importances indices [ ]])importance std np std (rf feature_importances_ for tree in rf estimators_ ]axis = plt figure (plt barrange ( shape [ ]importances indices ]color =" "yerr=stdindices ]align =center "plt xticks range ( shape [ ]indices + plt xlim ([- shape [ ]]pylab xlabel (feature index "pylab ylabel (importance "plt show ( feature index figure importance measure for the -feature data set with only informative features and