Explainable machine learning models for Medicare fraud detection

Journal of Big Data

Table 8 Mean AUPRC values by classifier and number of features for ten iterations of fivefold cross validation, for classifying the Medicare Part D data (Part 2)

Features classifier	15	20	25	30	82
CatBoost	0.8016	0.7953	0.7962	0.7949	0.7797
ET	0.4954	0.4647	0.4605	0.4391	0.3275
LightGBM	0.4447	0.4661	0.4603	0.4841	0.4982
Logistic Regression	0.3669	0.3536	0.3519	0.2939	0.3047
Random Forest	0.6097	0.5398	0.5519	0.5249	0.2429
XGBoost	0.7889	0.7448	0.7589	0.7548	0.7376