What is the main drawback of using accuracy as a performance metric?

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Question: What is the main drawback of using accuracy as a performance metric?

Options:

Correct Answer: It does not consider false positives and false negatives

Solution:

Accuracy can be misleading, especially in imbalanced datasets, as it does not account for the distribution of classes.

What is the main drawback of using accuracy as a performance metric?

What is the main drawback of using accuracy as a performance metric?

Step 1: Understand what accuracy means. Accuracy is the percentage of correct predictions made by a model.
Step 2: Recognize that accuracy is calculated as (Number of Correct Predictions) / (Total Predictions).
Step 3: Identify what an imbalanced dataset is. An imbalanced dataset has a large difference in the number of examples for each class.
Step 4: Realize that in an imbalanced dataset, a model can achieve high accuracy by predicting only the majority class.
Step 5: Understand that high accuracy does not mean the model is performing well if it fails to predict the minority class.
Step 6: Conclude that accuracy can be misleading because it does not show how well the model is doing for each class.

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