Which metric is most appropriate for evaluating a multi-class classification model?

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Q: Which metric is most appropriate for evaluating a multi-class classification model?

Solution: A confusion matrix provides a comprehensive view of the performance of a multi-class classification model.

Steps: 5

Step 1: Understand what a multi-class classification model is. This is a model that predicts one out of several possible classes (categories).
Step 2: Learn about the confusion matrix. It is a table that shows the number of correct and incorrect predictions for each class.
Step 3: Recognize that the confusion matrix helps you see how well the model is performing for each class individually.
Step 4: Note that the confusion matrix allows you to calculate other important metrics like accuracy, precision, recall, and F1-score for each class.
Step 5: Conclude that the confusion matrix is the most appropriate metric for evaluating a multi-class classification model because it provides a detailed view of performance.