What is Mean Squared Error used for?

Mean Squared Error (MSE) is primarily used to evaluate regression models, making it the correct choice for this question. MSE quantifies the average squared difference between the predicted values generated by a model and the actual observed values. This metric provides insight into how well the model is performing by highlighting the magnitude of errors—larger errors are disproportionately emphasized due to squaring the differences.

In neural networks and other functional approximations, MSE is a common choice for the loss function, as it facilitates the optimization process during training. By minimizing the MSE, practitioners aim to enhance the accuracy of their predictions, ensuring that the model generalizes well to new, unseen data.

The other options relate to different aspects of machine learning. Assessing classification model accuracy involves metrics like accuracy, precision, recall, or F1 score, which are geared towards categorical outcomes rather than continuous predictions. Calculating probabilities in logistic functions pertains to binary classification models and confusion matrix evaluation, which again does not concern the continuous nature of MSE. Anomaly detection typically employs different methods or metrics specifically designed to identify outliers, rather than relying solely on MSE.