Define precision, recall, and F1-score.

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✅ Precision, Recall, and F1-Score

These are classification metrics derived from the confusion matrix:

	Predicted Positive	Predicted Negative
Actual Positive	True Positive (TP)	False Negative (FN)
Actual Negative	False Positive (FP)	True Negative (TN)

🔹 1. Precision (a.k.a. Positive Predictive Value)

• Definition: Out of all predicted positives, how many are actually positive?

$$Precision = \frac{TP}{TP + FP}$$

• Focus: Quality of positive predictions.

• Example: In spam detection, if the model predicts 100 emails as spam, but only 80 are actually spam → Precision = 80%.

🔹 2. Recall (a.k.a. Sensitivity / True Positive Rate)

• Definition: Out of all actual positives, how many did the model correctly identify?

$$Recall = \frac{TP}{TP + FN}$$

• Focus: Coverage of actual positives.

• Example: If there are 100 spam emails, and the model correctly detects 70 → Recall = 70%.

🔹 3. F1-Score

• Definition: Harmonic mean of Precision and Recall. Balances both.

$$F1 = 2 \times \frac{Precision \times Recall}{Precision + Recall}$$

• Why Harmonic Mean? → It penalizes extreme values. If precision is high but recall is very low (or vice versa), F1 stays low.

• Example: If Precision = 0.8 and Recall = 0.7 →

$$F1 = 2 \times \frac{0.8 \times 0.7}{0.8 + 0.7} = 0.746$$

✅ When to Use Each

• Precision → When false positives are costly. (e.g., sending a wrong person to jail, marking genuine emails as spam).

• Recall → When false negatives are costly. (e.g., missing cancer diagnosis, fraud detection).

• F1-Score → When you need a balance (common in imbalanced datasets).

📌 Short Interview Answer

“Precision is the ratio of correctly predicted positives to all predicted positives. Recall is the ratio of correctly predicted positives to all actual positives. F1-score is their harmonic mean, balancing precision and recall. Precision is useful when false positives are costly, recall when false negatives are costly, and F1 when both matter.”

Read more :

What is accuracy in classification?

What is boosting (e.g., AdaBoost, XGBoost)?

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