PMut

Sample Predictor

Classifier evaluation table

Classifier	CV	Training	Test	Accuracy	Precision	Specificity	ROC AUC	MCC
Random Forest	Stratified k-fold	88 ± 1 (90.0% ± 1.1%)	9 ± 1 (10.0% ± 1.1%)	0.84 ± 0.12	0.87 ± 0.15	0.78 ± 0.24	0.84 ± 0.13	0.70 ± 0.24
Random Forest	k-fold	88 ± 0 (90.0% ± 0.4%)	9 ± 0 (10.0% ± 0.4%)	0.84 ± 0.10	0.88 ± 0.13	0.76 ± 0.19	0.84 ± 0.10	0.68 ± 0.17
Random Forest	uniref50 k-fold	88 ± 0 (90.0% ± 0.4%)	9 ± 0 (10.0% ± 0.4%)	0.79 ± 0.16	0.82 ± 0.20	0.76 ± 0.24	0.81 ± 0.14	0.62 ± 0.27

Classifier evaluation