Precision Recall
Precision and recall are evaluation metrics used in machine learning and information retrieval to assess the performance of classification models, particularly for imbalanced datasets. Precision measures the proportion of true positive predictions among all positive predictions, while recall measures the proportion of true positives identified among all actual positives. Together, they provide a nuanced view of model accuracy beyond simple overall accuracy.
Developers should learn and use precision and recall when working on classification tasks where false positives or false negatives have significant consequences, such as in medical diagnosis, fraud detection, or spam filtering. They are essential for evaluating models on imbalanced datasets where one class dominates, as accuracy alone can be misleading. These metrics help in tuning models to balance between minimizing false alarms (high precision) and capturing all relevant cases (high recall).