Which evaluation metric is most sensitive to class imbalance?

Which evaluation metric is most sensitive to class imbalance?

Step 1: Understand what class imbalance means. This occurs when one class in a dataset has significantly more examples than another class.
Step 2: Learn about evaluation metrics. These are ways to measure how well a model is performing.
Step 3: Recognize that accuracy is a common metric. It tells you the percentage of correct predictions made by the model.
Step 4: Realize that in imbalanced datasets, accuracy can be misleading. If a model predicts only the majority class, it can still have high accuracy.
Step 5: Identify that other metrics like precision, recall, or F1-score are often better for evaluating models on imbalanced datasets.

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