# On overfitting and post-selection uncertainty assessments

arXiv: https://arxiv.org/abs/1712.02379 · HTML: https://mlsanity.com/paper/1712.02379/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2017.

## Built on by (most cited first, 6 of 6)

- [Quantifying Overfitting: Evaluating Neural Network Performance through Analysis of Null Space](https://mlsanity.com/paper/2305.19424/) (2023)
- [Conformal prediction of future insurance claims in the regression problem](https://mlsanity.com/paper/2503.03659/) (2025)
- [Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging](https://mlsanity.com/paper/2205.14220/) (2022)
- [On some practical challenges of conformal prediction](https://mlsanity.com/paper/2510.10324/) (2025)
- [Predictive Modeling, Pattern Recognition, and Spatiotemporal Representations of Plant Growth in Simulated and Controlled Environments: A Comprehensive Review](https://mlsanity.com/paper/2412.10538/) (2024)
- [A new strategy for finite-sample valid prediction of future insurance claims in the regression setting](https://mlsanity.com/paper/2601.21153/) (2026)

## Abstract (arXiv)

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.