Matrix : Predict L1 L2 L3 Actual L1 3 0 2 L2 0 1 1 L3 0 2 3 Normalized Matrix : Predict L1 L2 L3 Actual L1 0.6 0.0 0.4 L2 0.0 0.5 0.5 L3 0.0 0.4 0.6 Overall Statistics : ACC Macro 0.72222 F1 Macro 0.56515 FPR Macro 0.20952 Kappa 0.35484 Overall ACC 0.58333 PPV Macro 0.61111 SOA1(Landis & Koch) Fair TPR Macro 0.56667 Zero-one Loss 5 Class Statistics : Classes L1 L2 L3 ACC(Accuracy) 0.83333 0.75 0.58333 AUC(Area under the ROC curve) 0.8 0.65 0.58571 AUCI(AUC value interpretation) Very Good Fair Poor F1(F1 score - harmonic mean of precision and sensitivity) 0.75 0.4 0.54545 FN(False negative/miss/type 2 error) 2 1 2 FP(False positive/type 1 error/false alarm) 0 2 3 FPR(Fall-out or false positive rate) 0.0 0.2 0.42857 N(Condition negative) 7 10 7 P(Condition positive or support) 5 2 5 POP(Population) 12 12 12 PPV(Precision or positive predictive value) 1.0 0.33333 0.5 TN(True negative/correct rejection) 7 8 4 TON(Test outcome negative) 9 9 6 TOP(Test outcome positive) 3 3 6 TP(True positive/hit) 3 1 3 TPR(Sensitivity, recall, hit rate, or true positive rate) 0.6 0.5 0.6 One-Vs-All : L1-Vs-All : Predict L1 ~ Actual L1 3 2 ~ 0 7 L2-Vs-All : Predict L2 ~ Actual L2 1 1 ~ 2 8 L3-Vs-All : Predict L3 ~ Actual L3 3 2 ~ 3 4