Table 6.

Results of Dataset 4.

Model	Acc	Prec	Rec	Spec	F1 Score	MCC	KAPPA	AUC	FPR	FNR	FDR	NVP
“lbfgs” LR	0.6088	0.5763	0.4075	0.766	0.4774	0.7231	0.9231	0.5868	0.9225	0.9226	0.9227	0.6234
“liblinear” LR	0.6088	0.5763	0.4075	0.766	0.4774	0.7229	0.9229	0.5868	0.9225	0.9221	0.9235	0.6234
“newton-cg” LR	0.6088	0.5757	0.4064	0.766	0.4765	0.723	0.9229	0.5862	0.9225	0.9215	0.9245	0.623
“Sag” LR	0.6083	0.5757	0.4064	0.766	0.4765	0.7231	0.9231	0.5862	0.9225	0.9205	0.9225	0.623
Decision Stump	0.5615	0.5615	0.5615	0.5615	0.5615	0.653	0.6084	0.5	0.6111	0.6511	0.6147	0.5615
Perceptron	0.5766	0.518	0.4944	0.6408	0.5059	0.7364	0.9364	0.5676	0.9211	0.9221	0.9211	0.6188
Ridge Classifier	0.5961	0.5456	0.4721	0.693	0.5062	0.7458	0.9457	0.5826	0.9334	0.9314	0.9354	0.627
Multinomial NB	0.5947	0.5994	0.2282	0.8808	0.4306	0.7656	0.7648	0.5545	0.6111	0.6101	0.6144	0.5937
Nearest Centroid	0.5834	0.5208	0.6269	0.5495	0.5689	0.7334	0.7316	0.5882	0.6133	0.6123	0.6153	0.6535
SGD	0.6049	0.561	0.4554	0.7217	0.5027	0.7214	0.9212	0.5885	0.6233	0.6223	0.6236	0.6292
SVC (kernel = ”linear”, C = 0.025)	0.5615	0.5615	0.5615	0.5615	0.5615	0.8432	0.8415	0.5	0.8103	0.8111	0.8123	0.5615
SVC (gamma = 2, C = 1)	0.6	0.6053	0.2527	0.8713	0.5566	0.8368	0.9367	0.562	0.9121	0.9123	0.9221	0.5989
LinearSVC	0.5859	0.5306	0.4821	0.6669	0.5052	0.8496	0.9496	0.5745	0.9221	0.9231	0.9231	0.6225
ZeroR	0.5615	0.5615	0.5615	0.5615	0.5615	0.5615	0.5615	0.5	0.6011	0.6211	0.6017	0.5615
Decision Tree	0.5556	0.4936	0.5178	0.5852	0.5054	0.8912	0.8911	0.5515	0.9092	0.9082	0.9092	0.6084
Passive Aggressive	0.5561	0.4939	0.4977	0.6017	0.4958	0.9453	0.9453	0.5497	0.9221	0.9231	0.9221	0.6054
Complement NB	0.6069	0.5686	0.4287	0.746	0.4888	0.7629	0.7617	0.5874	0.7071	0.7291	0.7471	0.6258
CatBoost	0.5937	0.5816	0.2616	0.853	0.3609	0.9176	0.9172	0.5573	0.9375	0.9376	0.9375	0.5967
Voting	0.5498	0.4866	0.4855	0.6	0.486	0.8406	0.9405	0.5427	0.9475	0.9495	0.9475	0.5989
SG	0.6123	0.5878	0.3875	0.7878	0.4671	0.851	0.951	0.5876	0.9465	0.9565	0.9465	0.6222
MLP	0.5571	0.495	0.3797	0.6008	0.498	0.7353	0.9353	0.5509	0.9523	0.9534	0.9523	0.6066
Bernoulli RBM	0.5947	0.5553	0.68	0.7626	0.451	0.9168	0.9165	0.5711	0.9533	0.9533	0.9533	0.6115
AdaBoost	0.5908	0.5877	0.2238	0.8773	0.3241	0.8921	0.892	0.5506	0.9269	0.927	0.9269	0.5914
Gradient Boosting	0.5942	0.5943	0.2349	0.8747	0.3367	0.9021	0.9015	0.5548	0.9265	0.9266	0.9265	0.5942
OLM	0.6083	0.5757	0.4064	0.766	0.4765	0.923	0.9229	0.5862	0.9254	0.9265	0.9254	0.623
Xgboost	0.5942	0.5943	0.2349	0.8747	0.3367	0.8995	0.8987	0.5548	0.9341	0.9356	0.9341	0.5942
Random Forest	0.6044	0.5805	0.353	0.8008	0.439	0.8831	0.8829	0.5769	0.9304	0.9314	0.9304	0.6131
Random Patches	0.5698	0.5149	0.3251	0.7606	0.3986	0.9205	0.9197	0.543	0.9441	0.9451	0.9441	0.5905
Extra Tree	0.5825	0.5354	0.3619	0.7547	0.4318	0.8839	0.8836	0.5583	0.678	0.679	0.678	0.6023

LR—logistic regression. SGD—Stochastic Gradient Descent. SVC—support vector machine. SG—stacked generalization. MLP—multilayer perceptron. OLM —ordinal learning model.