Regularization Rate

Regularization Rate in machine learning is a hyperparameter that determines the strength of regularization applied to a model. It balances the original loss function with the regularization term, influencing how much the model prioritizes simplicity over fitting the training data perfectly.

Balancing Act: Regularization Rate

The choice of regularization rate is crucial: too high a rate can lead to underfitting, where the model is too simple to capture the underlying pattern. Conversely, too low a rate may lead to overfitting. The optimal rate often requires experimentation and may vary depending on the specific dataset and model used.