Write Short note on

a. Confusion Matrix b. AVC-ROC curve.

Confusion Matrix Summary

1. Definition: A table showing the performance of a classification model.
2. Components:
   • True Positives (TP): Correct positive predictions.
   • True Negatives (TN): Correct negative predictions.
   • False Positives (FP): Incorrect positive predictions.
   • False Negatives (FN): Incorrect negative predictions.
3. Purpose: Helps calculate key metrics like accuracy, precision, recall, and F1-score.
4. Utility: Essential for evaluating model performance, particularly in imbalanced datasets.

AUC-ROC Curve Summary

1. Definition: Graphical representation of the classification model's ability to distinguish between classes.
2. Components:
   • ROC Curve: Plots true positive rate against false positive rate.
   • AUC (Area Under Curve): Measures the area beneath the ROC curve.
3. Interpretation: Higher AUC indicates better model performance.
4. Relevance: Useful for evaluating binary classifiers, independent of the classification threshold.