A confusion matrix is the correct metric for evaluating the performance of a classification model, such as the deep learning model built to classify types of materials in images.
Confusion Matrix:
It is a table used to describe the performance of a classification model by comparing the actual and predicted classifications.
Provides detailed insights into the model’s performance, including true positives, true negatives, false positives, and false negatives.
Why Option A is Correct:
Performance Measurement: Helps measure various performance metrics like accuracy, precision, recall, and F1-score, which are critical for evaluating a classification model.
Comprehensive Evaluation: Allows for a thorough analysis of where the model is making errors and the types of errors being made.
Why Other Options are Incorrect:
B. Correlation matrix: Used to identify relationships between variables, not for evaluating classification performance.
C. R2 score: Used for regression models, not classification.
D. Mean squared error (MSE): Also a metric for regression, measuring the average of the squares of the errors.
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