What is precision, recall, and F1 score?

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Precision, Recall, and F1-score are key metrics for evaluating classification models, especially when dealing with imbalanced datasets.

• Precision – The proportion of correctly predicted positive cases out of all predicted positives.
  Formula: Precision = TP / (TP + FP)
  High precision means fewer false positives.

• Recall (Sensitivity or True Positive Rate) – The proportion of correctly predicted positive cases out of all actual positives.
  Formula: Recall = TP / (TP + FN)
  High recall means fewer false negatives.

• F1-score – The harmonic mean of precision and recall, balancing both metrics.
  Formula: F1 = 2 × (Precision × Recall) / (Precision + Recall)
  Useful when you need a balance between precision and recall.

Example:
If a spam filter catches 90 spam emails (TP) out of 100 actual spam but flags 10 legitimate emails as spam (FP), it has high recall but lower precision.

Use Cases:

• High Precision Needed: Fraud detection (avoid false accusations).

• High Recall Needed: Medical diagnosis (avoid missing true cases).

In short, precision measures accuracy of positive predictions, recall measures coverage of actual positives, and F1-score balances the two.

Read More :

Explain confusion matrix.

What is cross-validation?

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