I tried a bunch of things: The dangers of unexpected overfitting in classification of brain data
Research output: Contribution to journal › Review article › peer-review
Authors
Colleges, School and Institutes
External organisations
- University of Kent
- Manada Technology LLC
- Penn State University
- Army Research Lab
Abstract
Machine learning has enhanced the abilities of neuroscientists to interpret information collected through EEG, fMRI, and MEG data. With these powerful techniques comes the danger of overfitting of hyperparameters which can render results invalid. We refer to this problem as 'over-hyping' and show that it is pernicious despite commonly used precautions. Over-hyping occurs when analysis decisions are made after observing analysis outcomes and can produce results that are partially or even completely spurious. It is commonly assumed that cross-validation is an effective protection against overfitting or overhyping, but this is not actually true. In this article, we show that spurious result can be obtained on random data by modifying hyperparameters in seemingly innocuous ways, despite the use of cross-validation. We recommend a number of techniques for limiting over-hyping, such as lock boxes, blind analyses, pre-registrations, and nested cross-validation. These techniques, are common in other fields that use machine learning, including computer science and physics. Adopting similar safeguards is critical for ensuring the robustness of machine-learning techniques in the neurosciences.
Bibliographic note
Details
Original language | English |
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Journal | Neuroscience and biobehavioral reviews |
Publication status | E-pub ahead of print - 6 Oct 2020 |