070d29f888489fb24621108d1554f4ec31b03e3c,auto_ml/predictor.py,Predictor,_print_ml_analytics_results_random_forest,#Predictor#,924

Before Change

        // XGB"s Classifier has a proper .feature_importances_ property, while the XGBRegressor does not.
        if self.trained_pipeline.named_steps["final_model"].model_name in ["XGBRegressor", "XGBClassifier"]:
            self._get_xgb_feat_importances(self.trained_pipeline.named_steps["final_model"].model)

        else:
            trained_feature_names = self._get_trained_feature_names()

After Change

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

        print("\n\nHere are the results from our " + final_model_obj.model_name)
        if self.name is not None:
            print(self.name)
        print("predicting " + self.output_column)

Instances