Summary of Composite Quantile Regression with Xgboost Using the Novel Arctan Pinball Loss, by Laurens Sluijterman et al.
Composite Quantile Regression With XGBoost Using the Novel Arctan Pinball Loss
by Laurens Sluijterman, Frank Kreuwel, Eric Cator, Tom Heskes
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the application of XGBoost for composite quantile regression, leveraging its flexibility, efficiency, and ability to handle missing data. The authors present a smooth approximation of the pinball loss function, tailored to XGBoost’s needs, which enables simultaneous prediction of multiple quantiles with fewer quantile crossings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a popular machine learning model called XGBoost to predict different levels of outcomes. It helps solve a problem where point estimates are not enough and we need more information about the distribution of the data. The authors find a new way to make the predictions work better, making it possible to get multiple predictions at once. |
Keywords
» Artificial intelligence » Loss function » Machine learning » Regression » Xgboost
