Regression Metrics
Regression metrics are statistical measures used to evaluate the performance of regression models, which predict continuous numerical outcomes. They quantify the difference between predicted values and actual observed values, helping assess model accuracy, error magnitude, and predictive quality. Common metrics include Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²).
Developers should learn regression metrics when building or deploying machine learning models for tasks like price prediction, sales forecasting, or risk assessment, as they provide objective criteria for model selection and optimization. They are essential for comparing different models, tuning hyperparameters, and ensuring models meet business requirements in fields such as finance, healthcare, and engineering.