Answer: TP = 900

## Explanation Based on the confusion matrix provided: - **TN (True Negative) = 100**: Correctly predicted no default when there was no default - **FP (False Positive) = 50**: Incorrectly predicted default when there was no default - **FN (False Negative) = 50**: Incorrectly predicted no default when there was default - **TP (True Positive) = 900**: Correctly predicted default when there was default To determine which value represents the correct predictions of defaults, we look at **TP (True Positive) = 900**, which corresponds to option D. **Key Metrics:** - **Accuracy** = (TP + TN) / Total = (900 + 100) / (100 + 50 + 50 + 900) = 1000 / 1100 ≈ 90.9% - **Precision** = TP / (TP + FP) = 900 / (900 + 50) = 900 / 950 ≈ 94.7% - **Recall/Sensitivity** = TP / (TP + FN) = 900 / (900 + 50) = 900 / 950 ≈ 94.7% - **Specificity** = TN / (TN + FP) = 100 / (100 + 50) = 100 / 150 ≈ 66.7% The model shows good performance in detecting defaults (high recall and precision) but has lower specificity in correctly identifying non-defaults.

Author: Tanishq Prabhu