Classification & Model Evaluation Calculators
Measure how well a test or classifier separates true cases from false ones.
A classifier or diagnostic test makes a yes-or-no prediction, and every prediction is either right or wrong in one of four ways: true positive, false positive, true negative, or false negative. The calculators here start from those four counts and derive the measures used to judge performance.
No single measure tells the whole story. Accuracy can look high on unbalanced data even when a model misses most of the rare cases, so it is worth reading several measures together.
Calculators in this category
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- Classification & EvaluationAccuracy CalculatorComing soon
- Classification & EvaluationConfusion Matrix CalculatorComing soon
- Classification & EvaluationSensitivity and Specificity CalculatorComing soon
- Classification & EvaluationYouden Index CalculatorComing soon
- Classification & EvaluationMcNemar's Test CalculatorComing soon
Key concepts
- Confusion matrix
- A table of the four outcome counts. Every other measure in this category is calculated from it.
- Sensitivity and specificity
- Sensitivity is the share of actual positives correctly identified. Specificity is the share of actual negatives correctly identified.
- Class imbalance
- When one class is much rarer than the other, overall accuracy can be misleading.