Calculate precision, recall, and F1 score for your machine learning models effortlessly.
Correctly predicted positive cases
Incorrectly predicted as positive
Correctly predicted negative cases
Incorrectly predicted as negative
Accuracy
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Precision
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Recall
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F1 Score
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The ratio of correct predictions to total predictions. Formula: (TP + TN) / (TP + FP + TN + FN)
How many of the positive predictions were actually correct. Formula: TP / (TP + FP)
How many actual positive cases were correctly identified. Formula: TP / (TP + FN)
Harmonic mean of precision and recall, providing a single score. Formula: 2 × (Precision × Recall) / (Precision + Recall)
From your confusion matrix, identify the four values: True Positives, False Positives, True Negatives, and False Negatives.
Enter each value in the corresponding field. Make sure all values are non-negative numbers.
Click "Calculate Metrics" to see all four performance metrics instantly displayed.