In a multi-class classification problem, which metric can be used to evaluate th

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Question: In a multi-class classification problem, which metric can be used to evaluate the model\'s performance across all classes?

Options:

Correct Answer: Macro F1 Score

Solution:

Macro F1 Score calculates the F1 Score for each class independently and then takes the average, providing a balanced evaluation across all classes.

In a multi-class classification problem, which metric can be used to evaluate the model's performance across all classes?

In a multi-class classification problem, which metric can be used to evaluate the model's performance across all classes?

Step 1: Understand that in a multi-class classification problem, there are multiple classes to predict.
Step 2: Know that we need a way to measure how well the model is performing for each class.
Step 3: Learn about the F1 Score, which combines precision and recall into one number for each class.
Step 4: Realize that the Macro F1 Score calculates the F1 Score for each class separately.
Step 5: Understand that after calculating the F1 Score for all classes, the Macro F1 Score takes the average of these scores.
Step 6: Conclude that the Macro F1 Score provides a balanced evaluation of the model's performance across all classes.

Multi-class Classification – A type of classification problem where instances can belong to one of three or more classes.
F1 Score – A metric that combines precision and recall to provide a single score that reflects the balance between them.
Macro F1 Score – An average of the F1 scores for each class, treating all classes equally regardless of their size.