Which metric is best suited for evaluating a multi-class classification model?
Practice Questions
Q1
Which metric is best suited for evaluating a multi-class classification model?
Mean Absolute Error
F1 Score
Root Mean Squared Error
R-squared
Questions & Step-by-Step Solutions
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.
