Genkin, Mikhail, Engel, Tatiana A (October 2019) Beyond generalization: Enhancing accurate interpretation of flexible models. bioRxiv. (Unpublished)
Abstract
Machine learning optimizes flexible models to predict data. In scientific applications, there is a rising interest in interpreting these flexible models to derive hypotheses from data. However, it is unknown whether good data prediction guarantees accurate interpretation of flexible models. We test this connection using a flexible, yet intrinsically interpretable framework for modeling neural dynamics. We find that many models discovered during optimization predict data equally well, yet they fail to match the correct hypothesis. We develop an alternative approach that identifies models with correct interpretation by comparing model features across data samples to separate true features from noise. Our results reveal that good predictions cannot substitute for accurate interpretation of flexible models and offer a principled approach to identify models with correct interpretation.
| Item Type: | Paper |
|---|---|
| CSHL Authors: | |
| Communities: | CSHL labs > Engel lab |
| Depositing User: | Matthew Dunn |
| Date: | 17 October 2019 |
| Date Deposited: | 07 Dec 2020 15:26 |
| Last Modified: | 08 Dec 2020 21:22 |
| URI: | https://repository.cshl.edu/id/eprint/39838 |
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