Precision:
Measures how many predicted positives are actually correct.
Precision = TP / (TP + FP)

Recall:
Measures how many actual positives were identified correctly.
Recall = TP / (TP + FN)

Simple Understanding:
Precision → How accurate are positive predictions?
Recall → How many actual positives were found?

Example: Disease detection:
High Recall → Fewer missed patients
High Precision → Fewer false alarms

40. Why is F1-score important?

F1-score combines Precision and Recall into one metric.
It is especially useful when classes are imbalanced or accuracy alone is misleading.

Formula:
F1 = 2 _ (Precision _ Recall) / (Precision + Recall)

Why It Matters:
A model with high precision but low recall, or high recall but low precision, may still perform poorly overall.
F1-score balances both.

Example: Fraud detection systems often use F1-score because fraud cases are rare.

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