List common evaluation metrics for regression tasks.

List common evaluation metrics for regression tasks.

• A. Mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and R-squared (R2)
• B. Accuracy only
• C. Precision and recall
• D. F1 and ROC-AUC

Answer: (A)

Evaluating regression models focuses on how close the predicted continuous values are to the actual ones. The most common metrics measure this error in different ways. Mean squared error calculates the average of the squared differences between predictions and actual values; squaring the errors places more emphasis on larger mistakes, which can be useful when big errors are particularly undesirable. Root mean squared error takes the square root of that result, returning the metric to the same units as the target and often making interpretation more intuitive. Mean absolute error averages the absolute differences, treating all errors equally and being less sensitive to outliers than MSE. R-squared, or the coefficient of determination, indicates how much of the variability in the target is explained by the model; higher values reflect a better fit, with 1 meaning perfect explanation of the variance in ideal conditions. Other metrics like accuracy, precision, recall, F1, and ROC-AUC come from classification tasks and don’t capture the magnitude of prediction errors on a continuous scale, so they aren’t appropriate for evaluating regression models.

Evaluating regression models focuses on how close the predicted continuous values are to the actual ones. The most common metrics measure this error in different ways. Mean squared error calculates the average of the squared differences between predictions and actual values; squaring the errors places more emphasis on larger mistakes, which can be useful when big errors are particularly undesirable. Root mean squared error takes the square root of that result, returning the metric to the same units as the target and often making interpretation more intuitive. Mean absolute error averages the absolute differences, treating all errors equally and being less sensitive to outliers than MSE. R-squared, or the coefficient of determination, indicates how much of the variability in the target is explained by the model; higher values reflect a better fit, with 1 meaning perfect explanation of the variance in ideal conditions.

Other metrics like accuracy, precision, recall, F1, and ROC-AUC come from classification tasks and don’t capture the magnitude of prediction errors on a continuous scale, so they aren’t appropriate for evaluating regression models.