Answer: Confusion matrix

## Explanation For classification problems like material type classification in images, the **confusion matrix** is the most appropriate metric to evaluate model performance. ### Why Confusion Matrix is Correct: 1. **Classification Problem**: The task involves classifying images into different material types (categorical classes), which is a classification problem. 2. **Comprehensive Evaluation**: A confusion matrix provides a complete picture of classification performance by showing: - True Positives (TP) - True Negatives (TN) - False Positives (FP) - False Negatives (FN) 3. **Derived Metrics**: From a confusion matrix, you can calculate various performance metrics: - Accuracy - Precision - Recall (Sensitivity) - F1-Score - Specificity ### Why Other Options are Incorrect: - **B. Correlation matrix**: Used to measure relationships between variables, not for classification model evaluation. - **C. R2 score**: Used for regression problems to measure how well the model explains variance in continuous data. - **D. Mean squared error (MSE)**: Primarily used for regression problems to measure average squared differences between predicted and actual continuous values. ### Key Takeaway: For classification models, confusion matrix and derived metrics (precision, recall, F1-score) are standard evaluation tools, while regression metrics (R2, MSE) are inappropriate for categorical classification tasks.

Author: Ritesh Yadav