Which metric would you use to evaluate a model's performance on a multi-class cl

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Question: Which metric would you use to evaluate a model\'s performance on a multi-class classification problem?

Options:

Correct Answer: Macro F1 score

Solution:

The macro F1 score is suitable for multi-class classification as it calculates the F1 score for each class and averages them.

Which metric would you use to evaluate a model's performance on a multi-class classification problem?

Which metric would you use to evaluate a model's performance on a multi-class classification problem?

Step 1: Understand that a multi-class classification problem involves more than two classes (categories) to predict.
Step 2: Learn about the F1 score, which is a measure that combines precision and recall for a single class.
Step 3: Realize that in a multi-class setting, you need to calculate the F1 score for each class separately.
Step 4: Calculate the F1 score for each class based on its true positives, false positives, and false negatives.
Step 5: Average the F1 scores of all classes to get the macro F1 score, which treats all classes equally.

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