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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