Summary of Hidden Variables Unseen by Random Forests, By Ricardo Blum et al.
Hidden Variables unseen by Random Forests
by Ricardo Blum, Munir Hiabu, Enno Mammen, Joseph Theo Meyer
First submitted to arxiv on: 19 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 Random Forests are renowned for modeling interactions effectively, but some straightforward examples indicate that they falter when faced with specific pure interactions that traditional CART criterion struggles to detect during tree construction. We propose that tweaking the partitioning schemes used in the tree-growing procedure can improve interaction identification. In a simulation study, we compare these variants to conventional Random Forests and Extremely Randomized trees. Our results demonstrate that the modifications enhance the model’s fitting capability in scenarios where pure interactions dominate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Random Forests are good at finding patterns in data. But sometimes they miss important connections between certain groups of data points. We think we can improve this by changing how the model splits the data into smaller groups. In a test, we compared our new approach to other methods and found that it does better when there are strong connections between some groups. |
