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I Tried a Bunch of Things: The Dangers of Unexpected Overfitting in Classification

Michael Skocik, John Collins, Chloe Callahan-Flintoft, Howard Bowman, View ORCID ProfileBrad Wyble
doi: https://doi.org/10.1101/078816
Michael Skocik
1Manada Technology LLC
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John Collins
2Physics Department, Penn State University
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Chloe Callahan-Flintoft
3Psychology Department, Penn State University
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Howard Bowman
4Computing Department, University of Kent
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Brad Wyble
3Psychology Department, Penn State University
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ABSTRACT

Machine learning is a powerful set of techniques that has enhanced the abilities of neuroscientists to interpret information collected through EEG, fMRI, MEG, and PET data. With these new techniques come new dangers of overfitting that are not well understood by the neuroscience community. In this article, we use Support Vector Machine (SVM) classifiers, and genetic algorithms to demonstrate the ease by which overfitting can occur, despite the use of cross validation. We demonstrate that comparable and non-generalizable results can be obtained on informative and non-informative (i.e. random) data by iteratively modifying hyperparameters in seemingly innocuous ways. We recommend a number of techniques for limiting overfitting, such as lock boxes, blind analyses, and pre-registrations. These techniques, although uncommon in neuroscience applications, are common in many other fields that use machine learning, including computer science and physics. Adopting similar safeguards is critical for ensuring the robustness of machine-learning techniques.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted October 03, 2016.
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I Tried a Bunch of Things: The Dangers of Unexpected Overfitting in Classification
Michael Skocik, John Collins, Chloe Callahan-Flintoft, Howard Bowman, Brad Wyble
bioRxiv 078816; doi: https://doi.org/10.1101/078816
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I Tried a Bunch of Things: The Dangers of Unexpected Overfitting in Classification
Michael Skocik, John Collins, Chloe Callahan-Flintoft, Howard Bowman, Brad Wyble
bioRxiv 078816; doi: https://doi.org/10.1101/078816

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