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Model Performance Metrics Calculator

Calculate accuracy, precision, recall, F1 score, MCC, AUC, and other classification performance metrics.

Inputs

Results

Accuracy

87.5%

Precision

89.47%

Recall (Sensitivity)

85%

F1 Score

87.18%

Matthews Correlation Coefficient75.09
AUC (Area Under Curve)87.5
How to Use This Calculator
  1. Enter the confusion matrix values: true positives, false positives, true negatives, false negatives.
  2. Review the derived metrics: accuracy, precision, recall, F1 score, and specificity.
  3. Set the positive class prevalence to see how threshold changes affect precision-recall tradeoff.
  4. For imbalanced datasets, prioritize F1 score or AUC-ROC over raw accuracy.
  5. Compare metrics across models to select the best performer on your evaluation criteria.

How the result changes with False Negatives (FN)

False Negatives (FN)AccuracyPrecisionRecall (Sensitivity)
691.62%89.47%93.41%
2184.95%89.47%80.19%
3978.13%89.47%68.55%
5473.22%89.47%61.15%

What each input means

True Positives (TP)
Correctly predicted positive cases
True Negatives (TN)
Correctly predicted negative cases
False Positives (FP)
Incorrectly predicted as positive
False Negatives (FN)
Incorrectly predicted as negative

How this is calculated

Formula

F1 = 2 × (Precision × Recall) / (Precision + Recall)

Worked example, using the default values

  1. Identify Input Parameters
    4 parameters
    True Positives (TP) = 85, True Negatives (TN) = 90, False Positives (FP) = 10, False Negatives (FN) = 15 = 4 input(s) provided
  2. Calculate Accuracy
    Accuracy
    87.5 = 87.5%
  3. Calculate Precision
    Precision
    89.47 = 89.47%
  4. Calculate Recall
    Recall
    85 = 85%
  5. Calculate Matthews Correlation Coefficient
    Matthews Correlation Coefficient
    75.09 = 75.09
  6. Calculate AUC
    AUC
    87.5 = 87.5

Engine last updated . Checked against 2 independently-derived tests — how we verify calculators.

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