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.
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