Which method is used for evaluating model performance in regression tasks?

In regression tasks, model performance is evaluated using methods that quantify how well the predicted values match the actual values. Mean Squared Error (MSE) is a widely used metric that calculates the average of the squares of the errors, which are the differences between predicted and actual values. This approach emphasizes larger errors because the differences are squared, thus providing a more sensitive measure of model performance.

Additionally, MSE is particularly useful as it offers a clear understanding of how far the predictions deviate from the true values on a squared scale, making it easier to optimize during model training. Lower values of MSE indicate better model performance, as they signify that predictions are closer to the actual values.

In contrast, metrics like accuracy, F1 Score, and confusion matrix are primarily designed for classification tasks rather than regression. Accuracy measures the proportion of correct predictions but does not provide insights into the degree of errors in regression scenarios. The F1 Score combines precision and recall for binary classification, and a confusion matrix summarizes performance for classification tasks by showing true positives, false positives, true negatives, and false negatives. These metrics are not applicable to regression evaluation where the nature of the output is continuous rather than categorical.