Summary of Joint Optimization Of Piecewise Linear Ensembles, by Matt Raymond et al.
Joint Optimization of Piecewise Linear Ensembles
by Matt Raymond, Angela Violi, Clayton Scott
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes JOPLEn (Joint Optimization of Piecewise Linear Ensembles), a method that jointly fits piecewise linear models at all leaf nodes of an existing tree ensemble. This allows for enhancements to the ensemble’s expressiveness and the application of penalties such as sparsity-promoting and subspace-norms to nonlinear predictions. JOPLEn is shown to be effective in feature selection for multitask learning and leads to improved prediction performance compared to standard random forest, boosted trees, and other methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make tree ensembles even better at making predictions. They developed a new way to fit piecewise linear models to the end of each branch in an existing tree ensemble. This makes the model more flexible and allows for penalties that promote sparsity or alignment with certain subspaces. The new method, called JOPLEn, is good at selecting features when there are many tasks to learn simultaneously, and it does a better job than usual tree ensembles. |
Keywords
» Artificial intelligence » Alignment » Feature selection » Optimization » Random forest
