Models selected by automated variable selection techniques do not need to be validated since they are ‘optimal’ models.

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Question restatement: The statement claims that models chosen by automated variable selection techniques do not require validation because they are ‘optimal’ models.
Option 1: True. This is incorrect because automated variable selection can overfit or capitalize on sampling peculiarities, leading to models that perform well on the training data but poorly on new data. Validation is essential to assess generalization, and claiming no validation is needed ignores the possibility of selection bias and overfitting.
Option 2: False. This is correct because even if a model is selected by an automated method, it still needs validation to confirm that its performance generalizes beyond the training data. Validation helps detect overfitting, estimate predictive accuracy on unseen data, and ensure robustness across different samples or settings. 
Further nuance: Validation can be internal (e.g., cross-validation) or external (using a separate test set). Relying solely on apparent optimality from the selection process is risky, since the selection process itself may exploit random variation. Therefore, validation remains a crucial step regardless of how the model was selected.
In summary, the statement claiming no validation is needed contradicts fundamental principles of model evaluation and generalization, making the assertion false.

2025WI_MSDS_410-DL_SEC56 Quiz #3: Advanced Modeling Topics

Models selected by automated variable selection techniques do not need to be validated since they are ‘optimal’ models.

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