Overview
Regularizers play a crucial role in predictive modeling by addressing the issue of overfitting, which occurs when a model learns the noise in the training data rather than the actual patterns. By adding penalties to the loss function, regularizers like L1 and L2 help create simpler models that gener...
Key Terms
Example: A model that performs well on training data but poorly on test data is overfitting.
Example: Regularization helps to simplify the model by discouraging overly complex models.
Example: L1 regularization can lead to some coefficients being exactly zero.
Example: L2 regularization shrinks the coefficients but does not eliminate them.
Example: Elastic Net is useful when there are multiple correlated features.
Example: The mean squared error is a common loss function used in regression.