Summary of Extending Explainable Ensemble Trees (e2tree) to Regression Contexts, by Massimo Aria et al.
Extending Explainable Ensemble Trees (E2Tree) to regression contexts
by Massimo Aria, Agostino Gnasso, Carmela Iorio, Marjolein Fokkema
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation (stat.CO); 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 The Ensemble methods such as random forests have been a game-changer in supervised learning, offering high-accuracy predictions by aggregating multiple weak learners. However, these methods often lack transparency, making it difficult for users to comprehend how RF models arrive at their predictions. Explainable ensemble trees (E2Tree) is a novel methodology that provides a graphical representation of the relationship between response variables and predictors, accounting for predictor effects on responses and associations between predictors through dissimilarity measures. Initially proposed for classification tasks, this paper extends E2Tree to regression contexts and demonstrates its explanatory power on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Explainable ensemble trees (E2Tree) is a new way to understand how random forests work. Right now, these powerful prediction tools don’t show us exactly how they make decisions. But E2Tree changes that by creating a picture of how the relationships between variables affect the outcome. This method was first used for classifying things into categories, but this paper shows it can also be used for predicting continuous values like heights or temperatures. It’s tested on real data to show how well it works. |
Keywords
» Artificial intelligence » Classification » Regression » Supervised
