Classification Metrics
Classification metrics are quantitative measures used to evaluate the performance of classification models in machine learning and statistics. They assess how well a model predicts categorical outcomes by comparing predicted labels against actual ground truth labels. Common metrics include accuracy, precision, recall, F1-score, and confusion matrix-based measures.
Developers should learn classification metrics when building or deploying classification models, such as in spam detection, medical diagnosis, or customer churn prediction, to objectively measure model effectiveness and guide improvements. They are essential for model validation, hyperparameter tuning, and comparing different algorithms to ensure reliable and fair predictions in real-world applications.