PMut2017

Classifier evaluation table
Classifier CV Training Test Accuracy Precision Specificity ROC AUC MCC
Random Forest k-fold 58752 ± 0 (90.0% ± 0.0%) 6528 ± 0 (10.0% ± 0.0%) 0.83 ± 0.00 0.80 ± 0.01 0.79 ± 0.01 0.82 ± 0.00 0.64 ± 0.01
Random Forest uniref50 k-fold 58752 ± 0 (90.0% ± 0.0%) 6528 ± 0 (10.0% ± 0.0%) 0.81 ± 0.02 0.78 ± 0.04 0.76 ± 0.03 0.80 ± 0.02 0.61 ± 0.04
Classifier evaluation