For regression problems, the most common evaluation metrics include mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), and R-squared (R²).
MSE measures the average of the squared differences between predicted and actual values, penalizing large errors more heavily. MAE computes the average of the absolute differences, providing a linear scale of errors. RMSE is the square root of MSE, offering a more interpretable scale that matches the units of the target variable.
R-squared measures the proportion of the variance in the dependent variable that is predictable from the independent variables. These metrics help evaluate the accuracy and performance of regression models by indicating how well the model fits the data.