Which metric is best suited for evaluating a multi-class classification model?

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Question: Which metric is best suited for evaluating a multi-class classification model?

Options:

Correct Answer: F1 Score

Solution:

F1 Score is a suitable metric for evaluating multi-class classification models as it considers both precision and recall.

Which metric is best suited for evaluating a multi-class classification model?

Which metric is best suited for evaluating a multi-class classification model?

Step 1: Understand what a multi-class classification model is. This is a model that predicts one out of several possible classes (more than two).
Step 2: Learn about precision. Precision measures how many of the predicted positive cases were actually positive.
Step 3: Learn about recall. Recall measures how many of the actual positive cases were correctly predicted by the model.
Step 4: Understand the F1 Score. The F1 Score is the harmonic mean of precision and recall, which means it balances both metrics.
Step 5: Recognize that in multi-class classification, you can calculate the F1 Score for each class and then average them to get a single score.
Step 6: Conclude that the F1 Score is a good choice because it takes into account both precision and recall, making it effective for evaluating the performance of multi-class models.

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