Summary of Ensembles Of Probabilistic Regression Trees, by Alexandre Seiller et al.
Ensembles of Probabilistic Regression Trees
by Alexandre Seiller, Éric Gaussier, Emilie Devijver, Marianne Clausel, Sami Alkhoury
First submitted to arxiv on: 20 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 This paper explores ensemble methods for probabilistic regression trees, aiming to create smooth approximations of objective functions. The authors develop and study various versions of these ensembles, demonstrating their consistency and comparing their performance to state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Probabilistic regression trees are a type of machine learning model that can be used for regression problems. This paper looks at ways to combine multiple probabilistic regression trees together to make them work even better. The authors show that these combined models are good and compare them to other methods that people have been using. |
Keywords
» Artificial intelligence » Machine learning » Regression
