Which metric is best suited for evaluating a model's performance on an imbalance
Practice Questions
Q1
Which metric is best suited for evaluating a model's performance on an imbalanced dataset?
Accuracy
Precision
Recall
F1 Score
Questions & Step-by-Step Solutions
Which metric is best suited for evaluating a model's performance on an imbalanced dataset?
Step 1: Understand what an imbalanced dataset is. This means 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 actual positive cases were correctly predicted by the model.
Step 4: Recognize that in imbalanced datasets, 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. The F1 Score combines precision and recall into one number, giving a better overall measure of performance for imbalanced datasets.
Step 6: Conclude that the F1 Score is a better metric for evaluating model performance on imbalanced datasets because it balances both precision and recall.
