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Classification Metrics
Full confusion matrix analysis — accuracy, precision, recall, F1, F-beta, MCC, Cohen's κ, likelihood ratios, and heatmap SVG.
Input Mode
Confusion Matrix Values
Confusion Matrix Heatmap
Accuracy
N (total)100
Accuracy0.85
Balanced Accuracy0.8535
Precision / Recall
Precision (PPV)0.9
Recall (TPR / Sensitivity)0.8182
Specificity (TNR)0.8889
FPR (Fall-out)0.1111
FNR (Miss rate)0.1818
NPV0.8
F-Scores
F1 (β=1)0.8571
F0.5 (precision-weighted)0.8824
F2 (recall-weighted)0.8333
Fβ 0.8571
Advanced
MCC0.7035
Cohen's κ0.7
Informedness (TPR+TNR−1)0.7071
Markedness (PPV+NPV−1)0.7
Likelihood Ratios
LR+ = TPR/FPR7.364
LR− = FNR/TNR0.2045
Diagnostic Odds Ratio36
F₁ = 2PR/(P+R) · MCC = (TP·TN−FP·FN)/√(…)
κ = (pₒ−pₑ)/(1−pₑ)
κ = (pₒ−pₑ)/(1−pₑ)