What are precision, recall, and F1-score?

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Precision, Recall, and F1-score are key metrics in machine learning and information retrieval, especially for evaluating classification models where class imbalance may exist.

1. Precision

• Definition: Out of all the predictions labeled as positive, how many were actually correct.

• Formula: TP / (TP + FP)

• Focus: Measures exactness → How many of the predicted positives are truly positive.

• Example: If a spam filter marks 100 emails as spam, and 90 are truly spam, precision = 90%.

2. Recall (a.k.a. Sensitivity or True Positive Rate)

• Definition: Out of all the actual positive cases, how many did the model correctly identify.

• Formula: TP / (TP + FN)

• Focus: Measures completeness → How many of the actual positives were captured.

• Example: If there are 100 spam emails in total, and the model correctly catches 80, recall = 80%.

3. F1-score

• Definition: Harmonic mean of Precision and Recall, providing a balance between the two.

• Formula: 2 × (Precision × Recall) / (Precision + Recall)

• Focus: Useful when you need a single score that balances precision and recall, especially with imbalanced datasets.

• Example: If precision = 90% and recall = 80%, F1 ≈ 85%.

👉 Key Insight:

• Precision answers: Of the ones I predicted positive, how many are right?

• Recall answers: Of all actual positives, how many did I find?

• F1-score balances the two, useful when both false positives and false negatives are important.

Read More :

What is cross-validation, and why do we use it?

What is the difference between regression and classification?

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