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Machine Learning Metrics
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The interactive reference ML practitioners bookmark. Formula derivations, instant calculators, and copy-paste code snippets — all on one page per topic.

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The 10 Most-Used ML Metrics

Enter your values and get results instantly. Click "Show formula" for the math behind each metric.

P
Classification

Precision

Of all positive predictions, how many were actually correct?

Precision 0.8000
▸ Show formula
Precision = TP / (TP + FP)
R
Classification

Recall (Sensitivity)

Of all actual positives, how many did the model correctly identify?

Recall 0.8889
▸ Show formula
Recall = TP / (TP + FN)
F1
Classification

F1 Score

Harmonic mean of precision and recall — balances both metrics.

F1 Score 0.8421
▸ Show formula
F1 = 2·TP / (2·TP + FP + FN)
= 2·(Prec·Rec) / (Prec + Rec)
A
Classification

AUC-ROC (Trapezoidal)

Approximate area under the ROC curve from TPR and FPR at a single threshold.

AUC (approx.) 0.8535
▸ Show formula
TPR = TP/(TP+FN), FPR = FP/(FP+TN)
AUC