Evaluating a machine learning model is crucial to determine its real-world performance, and this involves using various evaluation metrics tailored to the specific problem type. For classification tasks, accuracy is the most common metric, as it provides a straightforward measure of correct predictions against total instances. However, while accuracy is simple and easy to compare across different models, it may oversimplify the evaluation as it does not account for the complexities involved. Therefore, precision and recall offer a more detailed analysis, particularly for binary classification problems. Precision measures the ratio of true positive predictions to the total predicted positives, while recall assesses the ratio of true positives to the actual positives. Although typically associated with binary classifications, precision and recall can be adapted for other classification tasks as well. Understanding these metrics allows for a more nuanced evaluation of a model's performance, ensuring that it not only predicts accurately but also captures relevant instances effectively.