Accuracy Metrics
Accuracy metrics are quantitative measures used to evaluate the performance of machine learning models, particularly in classification tasks. They assess how well a model's predictions match the true labels, often expressed as a percentage or ratio of correct predictions to total predictions. Common examples include accuracy, precision, recall, F1-score, and confusion matrices.
Developers should learn accuracy metrics when building or deploying machine learning models to ensure reliable and effective performance in applications like spam detection, medical diagnosis, or fraud prevention. They are essential for model validation, comparison, and optimization, helping identify issues like overfitting or class imbalance that could impact real-world usability.