Replacing Cross-Validation with Interrogation
By Megan Czasonis, Yin Li, Huili Song, David Turkington
May 7, 2025

By Megan Czasonis, Yin Li, Huili Song, and David Turkington

 

Our innovative "interrogation" method detects unreliable machine learning predictions in advance, overcoming limitations of the traditional cross-validation method.

 

We introduce a new method called "interrogation" to warn when a machine learning model has underfit or overfit a data sample, offering a more efficient alternative to traditional cross-validation. Unlike cross-validation, which can be cumbersome and computationally expensive, interrogation evaluates models trained on all available data by breaking down their prediction logic into linear, nonlinear, pairwise, and high-order interaction components. This method successfully identified near-optimal stopping times for training neural networks without using validation samples, boosting confidence that models are well-calibrated and can perform reliably on new data. Interrogation is model-agnostic, providing transparency and reliability even for black-box models.

 

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