Which of the following expresses a true statement about ridge regression?

To understand why the selected answer is correct, it's essential to recognize the fundamental characteristics of ridge regression. Ridge regression is a technique used to address multicollinearity among predictor variables in regression models. It does this by adding a penalty term, specifically the square of the magnitude of coefficients, to the loss function, which helps to reduce the complexity of the model.

Ridge regression is considered less flexible compared to ordinary least squares (OLS) regression. This reduced flexibility comes from the way the penalty term restricts the size of the coefficients, limiting their variability. However, this restriction can lead to improved predictive accuracy, especially when dealing with datasets that have noisy or redundant features. By curbing the model's ability to fit the noise in the training data, ridge regression can provide more stable predictions on new, unseen data.

The relationship between flexibility and accuracy is nuanced. While less flexibility typically means that the model may not fit the data as closely as a more flexible model, it often prevents overfitting. As a result, ridge regression can achieve better performance on validation and test sets, particularly when the underlying true relationship is more straightforward than the complexity of the features might suggest.

This understanding highlights how ridge regression's inherent design focuses on trade-offs among flexibility,