Summary of Orthogonal Gradient Boosting For Simpler Additive Rule Ensembles, by Fan Yang et al.
Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles
by Fan Yang, Pierre Le Bodic, Michael Kamp, Mario Boley
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Gradient boosting of prediction rules is an efficient approach to learn potentially interpretable yet accurate probabilistic models. However, actual interpretability requires limiting the number and size of generated rules. The existing boosting variants are not designed for this purpose. This paper addresses this issue by introducing a new objective function that measures the angle between the risk gradient vector and the projection of the condition output vector onto the orthogonal complement of the already selected conditions. This approach favours the inclusion of more general and shorter rules, which improves the comprehensibility/accuracy trade-off of the fitted ensemble. The paper demonstrates this using a wide range of prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions by learning from many small rules instead of just one big rule. It makes sure these rules are easy to understand and not too complicated. This is important because sometimes we want our models to be simple enough for humans to understand. The new method makes this happen without slowing down the process. It works well with different types of prediction tasks. |
Keywords
* Artificial intelligence * Boosting * Objective function
