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.
