Which metric would be most appropriate for evaluating a model in an imbalanced classification scenario?
Practice Questions
1 question
Q1
Which metric would be most appropriate for evaluating a model in an imbalanced classification scenario?
Accuracy
F1 Score
Mean Squared Error
R-squared
F1 Score is more appropriate in imbalanced classification scenarios as it considers both precision and recall.
Questions & Step-by-step Solutions
1 item
Q
Q: Which metric would be most appropriate for evaluating a model in an imbalanced classification scenario?
Solution: F1 Score is more appropriate in imbalanced classification scenarios as it considers both precision and recall.
Steps: 6
Step 1: Understand what imbalanced classification means. This is when one class has many more examples than the other class.
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.
Step 4: Recognize that in imbalanced scenarios, accuracy can be misleading because a model can predict the majority class well but fail on the minority class.
Step 5: Understand the F1 Score. It combines precision and recall into one number, giving a better picture of model performance in imbalanced situations.
Step 6: Conclude that the F1 Score is the best metric to use because it balances both precision and recall.
