Answer Posted / Divya Prakash Gupta

Model Performance encompasses a range of metrics including accuracy, precision, recall, F1 score, ROC-AUC, etc., which provide a comprehensive evaluation of the model's ability to generalize well. While Accuracy is one aspect of model performance, it may not always be the most appropriate or useful metric due to class imbalance, skewed data distributions, or other factors. Therefore, it's crucial to consider multiple performance metrics when evaluating a machine learning model.

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