Why does the code 4 notebook use cross-validation when selecting the alpha value for regularization?

Question

BlackTom AI · Accepted Answer

When tuning hyperparameters like alpha for regularization, we want a dependable estimate of how the model will perform on unseen data rather than just looking at training metrics.
Option 1: To better estimate performance on new data. This is correct because cross-validation partitions data, trains on different folds, and assesses generalization, guiding the choice of alpha to improve performance on future samples.
Option 2: To minimize training error. This is misleading; cross-validation is not about minimizing training error on the training set. In fact, regularization often increases training error to reduce overfitting, and CV helps evaluate generalization rather than polishing training error.
Option 3: To reduce training time. Cross-validation typically increases computational cost because the model is trained multiple times on different folds, so this statement is inaccurate. The goal is generalization, not speed in training.
Option 4: To ensure all features are selected. Regularization methods like Lasso can drive some coefficients to zero, effectively performing feature selection; cross-validation is not used to ensure all features are kept. CV helps pick alpha that balances bias and variance, not guaranteeing all features remain.

BU.232.775.52.SP25 Quiz 3

Why does the code 4 notebook use cross-validation when selecting the alpha value for regularization?

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