Explain confusion matrix.

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A confusion matrix is a table used to evaluate the performance of a classification model by comparing predicted labels to actual labels. It shows how well a model distinguishes between classes.

For a binary classification, it has four components:

1. True Positive (TP) – Model correctly predicts positive cases.

2. True Negative (TN) – Model correctly predicts negative cases.

3. False Positive (FP) – Model predicts positive, but it’s actually negative (Type I error).

4. False Negative (FN) – Model predicts negative, but it’s actually positive (Type II error).

From he confusion matrix, you can calculate:

• Accuracy = (TP + TN) / Total

• Precision = TP / (TP + FP)

• Recall = TP / (TP + FN)

• F1-score = 2 × (Precision × Recall) / (Precision + Recall)

Purpose: It gives a detailed breakdown of model errors, helping identify if a model is better at detecting positives, negatives, or both.

In short, a confusion matrix is a key tool to assess classification performance beyond simple accuracy.

Read More :

What is bias-variance tradeoff?

What is cross-validation?

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