Precision & Recall Calculator

Calculate precision, recall, and F1 score from true positives, false positives, and false negatives. Essential for evaluating machine learning classification models.

True Positives (TP)

False Positives (FP)

False Negatives (FN)

How to Use the Precision & Recall Calculator

Enter the number of True Positives (TP).
Enter the number of False Positives (FP).
Enter the number of False Negatives (FN).
Click Calculate to get precision, recall, and F1 score.

使用例

•Evaluating binary classification models in machine learning.
•Comparing different models on imbalanced datasets.
•Tuning classification thresholds for business requirements.
•Reporting model performance in research papers.

計算式

Precision = TP / (TP + FP). Recall = TP / (TP + FN). F1 = 2 × Precision × Recall / (Precision + Recall).

よくある質問

What is precision?

Precision measures the ratio of true positives among all predicted positives: TP / (TP + FP).

What is recall?

Recall measures the ratio of true positives among all actual positives: TP / (TP + FN).

When should I prefer recall over precision?

Prefer recall when missing a positive is costly, e.g., in medical diagnosis or fraud detection.