Table 2.

Classification reports of all classifiers.

Classification algorithm	TNR	FPR	FNR	TPR	NPV	PPV	F1	Accuracy	AUC
Random forest	0.9104	0.0896	0.1219	0.8781	0.8794	0.9106	0.8937	0.8940	0.8943
XG boost	0.9009	0.0991	0.1500	0.8500	0.8546	0.8994	0.8734	0.8751	0.8754
Gradient boosting	0.8720	0.1280	0.1344	0.8656	0.8656	0.8753	0.8694	0.8687	0.8688
Extra trees	0.8848	0.1152	0.1469	0.8531	0.8550	0.8842	0.8681	0.8688	0.8690
Hist grad boosting	0.8849	0.1151	0.1719	0.8281	0.8357	0.8837	0.8535	0.8561	0.8565
Bagging	0.9041	0.0959	0.2031	0.7969	0.8131	0.8972	0.8431	0.8497	0.8505
Ada boost	0.8432	0.1568	0.1625	0.8375	0.8357	0.8505	0.8421	0.8402	0.8404
Gaussian process	0.9487	0.0513	0.2469	0.7531	0.7923	0.9394	0.8340	0.8497	0.8509
XG boost RF	0.8401	0.1599	0.1781	0.8219	0.8218	0.8441	0.8315	0.8308	0.8310
Decision tree	0.8591	0.1409	0.2125	0.7875	0.7973	0.8552	0.8189	0.8228	0.8233
Linear discriminant	0.7823	0.2177	0.1719	0.8281	0.8162	0.7973	0.8119	0.8055	0.8052
Logistic regression	0.7919	0.2081	0.1844	0.8156	0.8078	0.8014	0.8081	0.8039	0.8037
K neighbors	0.8911	0.1089	0.2906	0.7094	0.7499	0.8716	0.7813	0.7991	0.8002
Linear SVC	0.6486	0.3514	0.1625	0.8375	0.8278	0.7428	0.7719	0.7439	0.7430
Quadratic discrimant	0.8591	0.1409	0.2875	0.7125	0.7448	0.8382	0.7701	0.7849	0.7858
SVC	0.7374	0.2626	0.2313	0.7688	0.7581	0.7514	0.7589	0.7532	0.7531
Stochastic gradient desc	0.8300	0.1700	0.3219	0.6781	0.7206	0.8060	0.7329	0.7532	0.7541
Extra tree	0.8045	0.1955	0.3156	0.6844	0.7129	0.7854	0.7303	0.7437	0.7444
Gaussian naive bayes	0.8463	0.1537	0.3469	0.6531	0.7040	0.8150	0.7246	0.7485	0.7497

TNR True Negative Rate, FPR False Positive Rate, FNR False Negative Rate, TPR True Positive Rate, NPV Negative Predictive Value, AUC Area Under the Curve.