Model evaluation is essential for understanding how well a machine learning model performs, particularly in classification tasks. Key metrics used in this evaluation are precision, recall, and F1-score. Precision measures the accuracy of the positive predictions made by the model, indicating how many of the predicted positives are actually correct. Recall, on the other hand, assesses the model’s ability to identify all relevant instances, reflecting how many actual positives were correctly predicted. The F1-score combines both precision and recall into a single metric, providing a balanced measure that accounts for both the model’s accuracy in positive predictions and its completeness in identifying all relevant cases. These metrics are crucial for evaluating the performance of models, especially when dealing with imbalanced datasets where one class may be more prevalent than others.