Which metric would you use to evaluate a multi-class classification model?

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Question: Which metric would you use to evaluate a multi-class classification model?

Options:

Correct Answer: Macro-averaged F1 Score

Solution:

Macro-averaged F1 Score is suitable for evaluating multi-class classification models as it averages the F1 scores across all classes.

Which metric would you use to evaluate a multi-class classification model?

Which metric would you use to evaluate a multi-class classification model?

Step 1: Understand that a multi-class classification model predicts multiple classes or categories.
Step 2: Learn about the F1 Score, which is a measure that combines precision and recall to evaluate a model's performance.
Step 3: Recognize that in multi-class classification, you can calculate the F1 Score for each class individually.
Step 4: Understand that the Macro-averaged F1 Score takes the F1 Score of each class and averages them, treating all classes equally.
Step 5: Conclude that using the Macro-averaged F1 Score is a good way to evaluate the overall performance of a multi-class classification model.

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