What does the mean squared error (MSE) function primarily measure in a machine learning model?

The mean squared error (MSE) function primarily measures the difference between predicted and actual values in a machine learning model. MSE is calculated by taking the average of the squares of the differences between predicted values (outputs from the model) and the actual values (true outcomes). This means that MSE quantifies how well the model's predictions align with the real data points.

The squaring of the differences is significant because it amplifies larger errors, making the MSE sensitive to outliers. A lower MSE indicates better model performance, as it reflects a smaller average prediction error overall. MSE is particularly useful for regression tasks, where predicting continuous values is involved.

In contrast, the other options do not align with the primary purpose of MSE. The variance of predicted data and the correlation between features focus on different aspects of data analysis rather than on prediction accuracy. The overall accuracy of the model may refer to broader performance metrics, especially in classification tasks, and does not specifically relate to the error measurement characteristic of MSE.