3 examples of 'feature importance sklearn' in Python
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796 def feature_importances(clf, X, y): 797 798 from sklearn.feature_selection import SelectKBest 799 from sklearn.feature_selection import f_classif 800 801 try: 802 clfimp = clf.feature_importances_ 803 except: 804 sk = SelectKBest(f_classif, k='all') 805 sk_fit = sk.fit(X, y) 806 clfimp = sk_fit.scores_ 807 808 return (clfimp)
411 def get_features_importance(estimator): 412 413 features_importance = estimator.coef_ 414 print(features_importance) 415 return features_importance
168 def feature_importance(self, df, model, convert=False): 169 X, y = self.split_x_y(df) 170 171 if convert: 172 X = self.one_hot_encode(X, self.categoricals(X)) 173 model.fit(X, y) 174 importances = model.feature_importances_ 175 std = np.std([tree.feature_importances_ for tree in model.estimators_], axis=0) 176 indices = np.argsort(importances) 177 178 print("Feature ranking:") 179 plt.figure(figsize=(16, 14)) 180 plt.title("Feature importances") 181 plt.barh( 182 range(X.shape[1]), 183 importances[indices], 184 color="r", 185 xerr=std[indices], 186 align="center", 187 ) 188 plt.yticks(range(X.shape[1]), [list(X)[i] for i in indices]) 189 plt.ylim([-1, X.shape[1]]) 190 plt.show()