During model validation, the validator discovers that the model development team used the same dataset for both feature selection and model training, without a separate hold-out set. What risk does this introduce?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because using the same data for feature selection and model training introduces data leakage — the model has implicitly seen information from the evaluation data, producing overly optimistic performance metrics that will not generalize to new data. A is the opposite problem — more data exposure typically reduces underfitting.
Full explanation below image
Full Explanation
B is correct because using the same data for feature selection and model training introduces data leakage — the model has implicitly seen information from the evaluation data, producing overly optimistic performance metrics that will not generalize to new data. A is the opposite problem — more data exposure typically reduces underfitting. C is not a direct consequence. D relates to data protection, not statistical methodology.