What is the primary limitation of using accuracy as an evaluation metric?

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Question: What is the primary limitation of using accuracy as an evaluation metric?

Options:

Correct Answer: It does not account for class imbalance

Solution:

Accuracy does not account for class imbalance, which can lead to misleading interpretations of model performance.

What is the primary limitation of using accuracy as an evaluation metric?

What is the primary limitation of using accuracy as an evaluation metric?

Step 1: Understand what accuracy means. Accuracy is the percentage of correct predictions made by a model out of all predictions.
Step 2: Recognize that accuracy is calculated as (True Positives + True Negatives) / Total Predictions.
Step 3: Identify what class imbalance is. Class imbalance occurs when one class (category) has many more examples than another class.
Step 4: Realize that in a dataset with class imbalance, a model can achieve high accuracy by mostly predicting the majority class.
Step 5: Understand that high accuracy can be misleading if the model is not performing well on the minority class.
Step 6: Conclude that relying solely on accuracy can give a false sense of model performance, especially in imbalanced datasets.

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