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The power of noise and the art of prediction

Xiao, Z.; Higgins, S.

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Authors

Z. Xiao



Abstract

Data analysis usually aims to identify a particular signal, such as an intervention effect. Conventional analyses often assume a specific data generation process, which implies a theoretical model that best fits the data. Machine learning techniques do not make such an assumption. In fact, they encourage multiple models to compete on the same data. Applying logistic regression and machine learning algorithms to real and simulated datasets with different features of noise and signal, we demonstrate that no single model dominates others under all circumstances. By showing when different models shine or struggle, we argue that it is important to conduct predictive analyses using cross-validation for better evidence that informs decision making.

Citation

Xiao, Z., & Higgins, S. (2017). The power of noise and the art of prediction. International Journal of Educational Research, 87, 36-46. https://doi.org/10.1016/j.ijer.2017.10.006

Journal Article Type Article
Acceptance Date Oct 25, 2017
Online Publication Date Dec 1, 2017
Publication Date Dec 1, 2017
Deposit Date Apr 25, 2018
Publicly Available Date Jun 1, 2019
Journal International Journal of Educational Research
Print ISSN 0883-0355
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 87
Pages 36-46
DOI https://doi.org/10.1016/j.ijer.2017.10.006

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