The 2023 PHM North America Data Challenge is intriguing because it requires one to predict outcomes and use data patterns that training models do not see. Modern machine learning models based on gradient boosting and neural networks are not designed to address such issues in usually circumstances. Our final approach to address the challenge consists of five steps. In our approach, we use an unsupervised method besides machine learning models to address the challenge.
How to Cite
Machine Learning, Extrapolation, Unsupervised Learning
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5) 1189-1232
Chen, T., Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. arXiv: Learning, 785-794. Retrieved 3 27, 2023, from https://arxiv.org/abs/1603.02754
Wolfinger, R. D. (2020). XGBoost Add-In for JMP Pro. https://community.jmp.com/t5/JMP-Add-Ins/XGBoost-Add-In-for-JMP-Pro/ta-p/319383
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32.
JMP (2023), Version 18. SAS Institute Inc., Cary, NC, 1989–2023.
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