aae3c2048e8fb54452087b2fa9ef66cb133a8a23,auto_ml/predictor.py,Predictor,ml_for_analytics,#Predictor#Any#Any#Any#Any#Any#,87

Before Change

                    feature_ranges = self.trained_pipeline.named_steps["final_model"].feature_ranges

                    print("feature_ranges")
                    print(feature_ranges)

                    for col_idx, feature_name in enumerate(trained_feature_names):
                        print(feature_name + ": " + str(trained_coefficients[col_idx]))

After Change

                    feature_ranges = self.trained_pipeline.named_steps["final_model"].feature_ranges

                    feature_summary = []
                    for col_idx, feature_name in enumerate(trained_feature_names):

                        potential_impact = feature_ranges[col_idx] * trained_coefficients[col_idx]
                        summary_tuple = (feature_name, trained_coefficients[col_idx], potential_impact)
                        feature_summary.append(summary_tuple)

                    sorted_feature_summary = sorted(feature_summary, key=lambda x: x[2])

                    for summary in sorted_feature_summary:
                        print(summary[0] + ": " + str(summary[1]))
                        print("The potential impact of this feature is: " + str(summary[2]))


        else:

Instances