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Which metric would you use to evaluate a multi-class classification model?
Which metric would you use to evaluate a multi-class classification model?
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Practice Questions
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Q1
Which metric would you use to evaluate a multi-class classification model?
F1 Score
Precision
Macro-averaged F1 Score
Mean Squared Error
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Macro-averaged F1 Score is suitable for evaluating multi-class classification models as it averages the F1 scores across all classes.
Questions & Step-by-step Solutions
1 item
Q
Q: Which metric would you use to evaluate a multi-class classification model?
Solution:
Macro-averaged F1 Score is suitable for evaluating multi-class classification models as it averages the F1 scores across all classes.
Steps: 5
Show Steps
Step 1: Understand that a multi-class classification model predicts multiple classes or categories.
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 multi-class classification, you can calculate the F1 Score for each class individually.
Step 4: Understand that the Macro-averaged F1 Score takes the F1 Score of each class and averages them, treating all classes equally.
Step 5: Conclude that using the Macro-averaged F1 Score is a good way to evaluate the overall performance of a multi-class classification model.
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