Regularizers such as Lasso and Ridge are used in predictive models to prevent ______ by penalizing complex parameter configurations.

Regularizers such as Lasso and Ridge are used in predictive models to prevent overfitting by penalizing complex parameter configurations. They add a cost to large or numerous weights, so the model is discouraged from fitting noise in the training data. Lasso shrinks some coefficients to zero, effectively selecting a simpler set of features, while Ridge shrinks all coefficients toward zero without eliminating any. This encourages the model to capture only the strongest patterns, making predictions more stable on new data. For instance, in a linear regression with 20 predictors, Ridge will reduce each weight’s magnitude, reducing variance and improving generalization.