A regularization rate is a hyperparameter that controls the strength of regularization during training. Increasing it can reduce overfitting but may also reduce predictive power if the model becomes too constrained.
A number that specifies the relative importance of regularization during training. Raising the regularization rate reduces overfitting but may reduce the model's predictive power. Conversely, reducing or omitting the regularization rate increases overfitting. The regularization rate is usually represented as the Greek letter lambda. The following simplified loss equation shows lambda's influence: where regularization is any regularization mechanism, including; - L~1~ regularization - L~2~ regularization See Overfitting: L2 regularization in Machine Learning Crash Course for more information.