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

₹0.0

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

Options:

Correct Answer: Macro F1 Score

Solution:

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 in a multi-class classification problem?

Which metric would you use to evaluate a model's performance in 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 to evaluate a model's performance.
Step 3: Recognize that in a multi-class setting, you can calculate the F1 score for each individual class.
Step 4: Calculate the F1 score for each class separately, which gives you a score for how well the model performs on that class.
Step 5: Average the F1 scores of all classes to get the Macro F1 Score, which treats all classes equally.

No concepts available.