ridgeMul

ridgeMul is a term that appears in the context of machine learning, specifically related to regularization techniques. It often refers to a multiplicative approach to applying ridge regression regularization. Ridge regression itself is a technique used to prevent overfitting in statistical models by adding a penalty term to the loss function. This penalty is proportional to the square of the magnitude of the coefficients.

In a multiplicative ridgeMul approach, this penalty is not simply added but is incorporated into the calculation in a way that involves multiplication. This can manifest in different algorithms or implementations where the regularization strength is applied as a factor during certain computational steps, rather than a direct additive term. The goal remains the same: to shrink large coefficients and reduce the model's sensitivity to individual data points, thereby improving generalization performance on unseen data. The specific mathematical formulation of ridgeMul would depend on the particular algorithm or library where it is encountered. It's a variation on the standard additive formulation of ridge regularization, aiming for potentially different convergence properties or numerical stability in certain contexts.