Table 2.

Classifier effectiveness comparison

Classifier	Accuracy	Precision	Recall	F1	TP	FP	TN	FN	AUC
SGD	0.06	0.03	0.738	0.678	1,020	605	564	362	0.610
Naive Bayes	0.02	0.004	0.871	0.704	1,205	833	336	177	0.580
Linear SVC	0.27	0.002	0.662	0.642	915	553	616	467	0.595
Random Forest	3.57	0.06	0.790	0.674	1,093	765	404	289	0.568
Logistic Regression	0.22	0.002	0.758	0.684	1,034	603	566	348	0.616
Nearest Neighbor	0.019	4.17	0.646	0.616	894	625	544	488	0.556
Decision Tree	9.18	0.009	0.616	0.604	852	585	584	530	0.558
Gradient Boost	22.7	0.02	0.860	0.701	1,189	818	351	193	0.580
Perceptron	0.08	0.05	0.749	0.638	1,088	770	399	294	0.571
Passive Aggressive	0.09	0.04	0.768	0.702	935	533	586	497	0.591