Comparison of Neural Networks and Gradient Boosting Models on Ordinal Age Class Prediction Using Mouse Trajectories

MCML Authors

Lisa Bondo Andersen

→ Group Frauke Kreuter
 Social Data Science and AI

Ailin Liu

→ Group Frauke Kreuter
 Social Data Science and AI

Frauke Kreuter

Prof. Dr.

Core PI

Social Data Science and AI

Abstract

We aim to predict ordinal age classes via mouse trajectories collected through web surveys. We compare performance of different neural network architectures, including dense neural networks using the entire trajectory as input, 1D- and 2D-convolutional neural networks, long short-term memory neural networks, and transformers against gradient boosting models that use hand-crafted features of the trajectories as inputs. The results show that neural networks as well as gradient boosting models are able to predict age classes with accuracies above pure chance. However, despite their higher complexity, neural networks do not clearly outperform the boosting models and do not offer the advantages of supplying interpretable results and detecting informative covariates.

inproceedings WBL+26

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NLDL 2026

Northern Lights Deep Learning Conference. Tromsø, Norway, Jan 06-08, 2026. To be published. Preprint available.

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Authors

T. Wistuba • L. Bondo Andersen • A. Liu • F. Henninger • F. Kreuter • S. Greven

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Links

URL

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Research Area

 C4 | Computational Social Sciences