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dev.classification-metrics Calculator
Calculates precision, recall, F1 score, and AUC-ROC for a binary classifier from confusion matrix values. Precision measures false positive rate; recall measures false negative rate — F1 is the harmonic mean, appropriate when both matter. AUC-ROC measures discrimination across all thresholds.
Inputs
True Pos
Reference formula or conversion factor shown for context.
True Neg
Reference formula or conversion factor shown for context.
False Pos
Reference formula or conversion factor shown for context.
False Neg
Reference formula or conversion factor shown for context.
Results
F1 score
A numerical rating from the scoring model in use.
precision
Sample size or count used in the calculation.
recall (sensitivity)
Sample size or count used in the calculation.
accuracy
The fraction of predictions or measurements that are correct. High accuracy is only meaningful when the class distribution is balanced.
specificity
Reference formula or conversion factor shown for context.