Question: Which evaluation metric is best for imbalanced classification problems?
Options:
Accuracy
F1 Score
Mean Squared Error
R-squared
Correct Answer: F1 Score
Solution:
F1 Score is better for imbalanced classification as it considers both precision and recall.
Which evaluation metric is best for imbalanced classification problems?
Practice Questions
Q1
Which evaluation metric is best for imbalanced classification problems?
Accuracy
F1 Score
Mean Squared Error
R-squared
Questions & Step-by-Step Solutions
Which evaluation metric is best for imbalanced classification problems?
Step 1: Understand what imbalanced classification means. This is when one class (like 'yes' or 'no') has many more examples than the other class.
Step 2: Learn about evaluation metrics. These are ways to measure how well a model is performing.
Step 3: Know that accuracy can be misleading in imbalanced problems because a model can predict the majority class and still seem accurate.
Step 4: Learn about precision. This measures how many of the predicted positive cases were actually positive.
Step 5: Learn about recall. This measures how many of the actual positive cases were correctly predicted.
Step 6: Understand that the F1 Score combines both precision and recall into one number, making it useful for imbalanced classes.
Step 7: Conclude that the F1 Score is a better evaluation metric for imbalanced classification problems because it balances the trade-off between precision and recall.
No concepts available.
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