The metrics for binary models is AUC, F-score for categorical models and RMSE for continuous models. The higher the AUC, the better a model is at predicting the outcome. ADM selects features based on their individual univariate performance against the outcome, measured as the area under the curve (AUC) of a ROC graph. By default, the univariate performance threshold is set to 0.52 AUC. Those statements are copied from the official Pega training documents of PCDS (Pega Certified Data Scientist) exam. AUC is treated as an important performance metrics toRead More