On overfitting and post-selection uncertainty assessments

Liang Hong; Todd A. Kuffner; Ryan Martin

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arXiv · 1712.02379

On overfitting and post-selection uncertainty assessments

Liang Hong·Todd A. Kuffner·Ryan Martin

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Abstract

In a regression context, when the relevant subset of explanatory variables is uncertain, it is common to use a data-driven model selection procedure. Classical linear model theory, applied naively to the selected sub-model, may not be valid because it ignores the selected sub-model's dependence on the data. We provide an explanation of this phenomenon, in terms of overfitting, for a class of model selection criteria.

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Liang Hong, Todd A. Kuffner, Ryan Martin. 2017-12-06. On overfitting and post-selection uncertainty assessments. https://arxiv.org/abs/1712.02379

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