Summary of Free Lunch in the Forest: Functionally-identical Pruning Of Boosted Tree Ensembles, by Youssouf Emine et al.
Free Lunch in the Forest: Functionally-Identical Pruning of Boosted Tree Ensembles
by Youssouf Emine, Alexandre Forel, Idriss Malek, Thibaut Vidal
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 introduced pruning algorithm preserves the functionality of tree ensembles by iteratively removing nodes while maintaining the prediction function’s identity. This lossless method reduces large ensemble sizes without compromising performance, offering a “free lunch” for tabular data applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a way to make big machine learning models smaller and more efficient, without changing how they work or perform. It’s like finding a magic eraser that can remove parts of the model without affecting its predictions. This is useful because large models are often hard to understand and take a long time to use. |
Keywords
» Artificial intelligence » Machine learning » Pruning
