Which method is specifically used for measuring the accuracy of a machine learning model?

Root Mean Square Error (RMSE) is a method used to measure the accuracy of a machine learning model, particularly in regression tasks. It provides a single value that reflects the average magnitude of the errors between the predicted values and the actual target values. By squaring the errors, RMSE gives more weight to larger errors, which can be particularly useful when it is important to penalize significant deviations in predictions. This method enables model developers to understand how well the model is performing in terms of prediction accuracy.

While other methods like Mean Absolute Error (MAE) and the F1 Score are commonly used for measuring error and performance, RMSE is particularly valuable for its sensitivity to outliers and is widely recognized in various contexts where the assumption of normally distributed errors is plausible. The confusion matrix, on the other hand, is a tool used for classification problems that provides insights into the true positives, false positives, true negatives, and false negatives, offering a direct measurement of classification accuracy, but not in a way that provides an aggregate performance metric as RMSE does.