BMC Bioinformatics

Table 2 Correlation between different classifiers based on 10-fold cross-validation results

From: StackDPP: a stacking ensemble based DNA-binding protein prediction model

	DT	LR	RF	SVC	SVC	SVC	SVC	SVC	EXT	GNB	ADB	LDA	KNN	BG	BG
				(RBF)	(RBF, tuned)	(linear)	(poly)	(sigmoid)							(SVC-RBF)
DT	1.00	0.68	0.78	0.77	0.76	0.66	0.74	0.74	0.78	0.74	0.58	0.66	0.75	0.78	0.77
LR	0.68	1.00	0.85	0.89	0.90	0.96	0.85	0.87	0.86	0.82	0.69	0.85	0.85	0.82	0.89
RF	0.78	0.85	1.00	0.97	0.95	0.84	0.94	0.94	0.99	0.92	0.74	0.85	0.94	0.96	0.97
SVC (RBF)	0.77	0.89	0.97	1.00	0.99	0.86	0.94	0.95	0.97	0.93	0.73	0.88	0.95	0.93	1.00
SVC(RBF, tuned)	0.76	0.90	0.95	0.99	1.00	0.87	0.92	0.94	0.95	0.90	0.73	0.88	0.94	0.92	0.99
SVC (linear)	0.66	0.96	0.84	0.86	0.87	1.00	0.84	0.85	0.84	0.79	0.70	0.82	0.82	0.81	0.86
SVC (poly)	0.74	0.85	0.94	0.94	0.92	0.84	1.00	0.94	0.95	0.89	0.74	0.82	0.93	0.91	0.94
SVC (sigmoid)	0.74	0.87	0.94	0.95	0.94	0.85	0.94	1.00	0.94	0.90	0.73	0.84	0.92	0.91	0.95
EXT	0.78	0.86	0.99	0.97	0.95	0.84	0.95	0.94	1.00	0.92	0.74	0.85	0.95	0.95	0.97
GNB	0.74	0.82	0.92	0.93	0.90	0.79	0.89	0.90	0.92	1.00	0.68	0.80	0.91	0.88	0.93
ADB	0.58	0.69	0.74	0.73	0.73	0.70	0.74	0.73	0.75	0.68	1.00	0.65	0.71	0.70	0.74
LDA	0.66	0.85	0.85	0.88	0.88	0.82	0.82	0.84	0.85	0.80	0.65	1.00	0.84	0.82	0.88
KNN	0.75	0.85	0.94	0.95	0.94	0.82	0.93	0.92	0.95	0.91	0.71	0.84	1.00	0.91	0.95
BG	0.78	0.82	0.96	0.93	0.92	0.81	0.91	0.91	0.95	0.88	0.70	0.82	0.91	1.00	0.93
BG (SVC-RBF)	0.77	0.89	0.97	1.00	0.99	0.86	0.94	0.95	0.97	0.93	0.74	0.88	0.95	0.93	1.00

DT: Decision tree, LR: Logistic Regression, RF: Random Forest, SVC: Support Vector Classifier, EXT: Extra tree, GNB: Gaussian Naive Bayes, ADB: Adaboost, KNN: K-nearest neighbour, BG: Bagging classifier

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