Evaluating a model is a core part of building an effective machine learning model. There are several evaluation metrics such as confusion matrix, cross-validation, and AUC-ROC curve. Different evaluation metrics help with analyzing the model performance.
Use the aif.show_model_performance Python API to view the different evaluation metrics.
The following are the inputs:
· performance_metric: Specify the name of the performance metric from the following options.
§ Prediction Deciles
§ Confusion Matrix
§ Prediction Density
§ ROC Curve
§ F1 Curve
§ Kappa Curve
§ Precision-Recall Curve
§ Ranking Power Tests
· cut_off_method: This parameter is applicable if the performance metric is the Confusion Matrix. The following are the options:
§ F1: Provides confusion matrix at max F1 as the cut-off.
§ KS: Provides confusion matrix at max KS as the cut-off.
§ KA: Provides confusion matrix at max Kappa as the cut-off.
· test_statistic: This parameter is applicable if the performance metric is Ranking Power Tests. The following are the options:
§ KS Test
§ Rank Order Test
§ Lorenz Curve
§ Lift Curve
· model: This parameter is applicable if the performance metric is Ranking Power Tests. The following are the options:
§ Input any Model ID by looking at AUC Summary. If this parameter is not specified, by default Best model is chosen from AUC Summary
· ranking_power_tests_as_matrix: This parameter is applicable if the performance metric is Ranking Power Tests and can be True or False. The default value is False.
The output returns the performance plots and/or matrix.