Precision & Recall Calculator
Calculate precision, recall, and F1 score from true positives, false positives, and false negatives. Essential for evaluating machine learning classification models.
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.
Casos de Uso
- •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.
Fórmula
Precision = TP / (TP + FP). Recall = TP / (TP + FN). F1 = 2 × Precision × Recall / (Precision + Recall).
Preguntas Frecuentes
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.