Which metric is best suited for evaluating a model's performance on an imbalance

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Question: Which metric is best suited for evaluating a model\'s performance on an imbalanced dataset?

Options:

Correct Answer: F1 Score

Solution:

F1 Score is a better metric for imbalanced datasets as it considers both precision and recall, providing a balance between them.

Which metric is best suited for evaluating a model's performance on an imbalanced dataset?

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.

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