e6bc2e29b7825e15f4e0e95d74e43910d69875cc,auto_ml/predictor.py,Predictor,_get_xgb_feat_importances,#Predictor#Any#,985

Before Change

        print("Here are the feature_importances from the tree-based model:")
        print("The printed list will only contain at most the top 50 features.")
        for feature in sorted_feature_infos[-50:]:
            print(str(feature[0]) + ": " + str(round(feature[1] / sum_of_all_feature_importances, 4)))


    def _print_ml_analytics_results_random_forest(self, feature_responses):
        try:
            final_model_obj = self.trained_final_model.named_steps["final_model"]
        except:

After Change

            feature_infos.append([feature_name, feat_importance])

        sorted_feature_infos = sorted(feature_infos, key=lambda x: x[1])
        df = pd.DataFrame(sorted_feature_infos)
        return df

        // print("Here are the feature_importances from the tree-based model:")
        // print("The printed list will only contain at most the top 50 features.")

Instances