Precision and recall are essential metrics used to evaluate the performance of classification algorithms in machine learning and information retrieval. Precision measures the accuracy of the positive predictions made by a model, indicating how many of the predicted positive instances are actually correct. In contrast, recall assesses the model’s ability to identify all relevant instances, reflecting how many true positive cases were successfully captured. These metrics are particularly important in scenarios with imbalanced class distributions, where traditional accuracy may not provide a complete picture of model performance. Understanding the trade-off between precision and recall is crucial for optimizing classification tasks.