Z. Xiao
The Power of Noise and the Art of Prediction
Xiao, Z.; Higgins, S.
Authors
S. Higgins
Abstract
Data analysis usually aims to identify a particular signal, such as an intervention effect. Conventional analyses often as- sume a specific data generation process, which suggests 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. Ap- plying 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 it is both possible and important to conduct comparative analyses.
Citation
Xiao, Z., & Higgins, S. (2008). 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 | 2008 |
Deposit Date | Jul 17, 2017 |
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 |
Keywords | Cross-Validation, Evidence-Based Policy, K -NN, Logistic Regression, Prediction, Random Forests |
Related Public URLs | https://osf.io/preprints/socarxiv/zu64w/ |
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Copyright Statement
© 2017 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
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