Xiao, ZhiMin and Higgins, Steve (2018) The power of noise and the art of prediction. International Journal of Educational Research, 87. pp. 36-46. DOI https://doi.org/10.1016/j.ijer.2017.10.006
Xiao, ZhiMin and Higgins, Steve (2018) The power of noise and the art of prediction. International Journal of Educational Research, 87. pp. 36-46. DOI https://doi.org/10.1016/j.ijer.2017.10.006
Xiao, ZhiMin and Higgins, Steve (2018) The power of noise and the art of prediction. International Journal of Educational Research, 87. pp. 36-46. DOI https://doi.org/10.1016/j.ijer.2017.10.006
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.
Item Type: | Article |
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Uncontrolled Keywords: | Cross-validation; Evidence-based policy; -NN; Logistic regression; Prediction; Random forests |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Health and Social Care, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 11 Aug 2021 14:20 |
Last Modified: | 16 May 2024 20:51 |
URI: | http://repository.essex.ac.uk/id/eprint/30842 |
Available files
Filename: The power of noise and the art of prediction.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 3.0