Which evaluation approach involves testing the model multiple times on different data slices to obtain a robust performance estimate?

Which evaluation approach involves testing the model multiple times on different data slices to obtain a robust performance estimate?

• A. K-fold Cross-Validation
• B. Ridge Regression (L2 penalty)
• C. LASSO (L1 penalty)
• D. Elastic Net (L1 + L2 penalties)

Answer: (A)

Testing a model on multiple data slices to obtain a robust performance estimate is cross-validation. In k-fold cross-validation, you split the dataset into k equal parts. The model is trained on k−1 parts and evaluated on the remaining part. This process is repeated so every part serves as the test set once. By averaging the performance across all folds, you get a more stable and generalizable estimate because it reduces the dependence on a single train-test split and uses the data more efficiently. Regularization methods like ridge, LASSO, and Elastic Net are about how the model fits by penalizing coefficients to prevent overfitting. They’re modeling techniques, not evaluation procedures, though you can use cross-validation to tune their penalties.

Testing a model on multiple data slices to obtain a robust performance estimate is cross-validation. In k-fold cross-validation, you split the dataset into k equal parts. The model is trained on k−1 parts and evaluated on the remaining part. This process is repeated so every part serves as the test set once. By averaging the performance across all folds, you get a more stable and generalizable estimate because it reduces the dependence on a single train-test split and uses the data more efficiently.

Regularization methods like ridge, LASSO, and Elastic Net are about how the model fits by penalizing coefficients to prevent overfitting. They’re modeling techniques, not evaluation procedures, though you can use cross-validation to tune their penalties.