Summary of A Survey and Taxonomy Of Methods Interpreting Random Forest Models, by Maissae Haddouchi and Abdelaziz Berrado
A survey and taxonomy of methods interpreting random forest models
by Maissae Haddouchi, Abdelaziz Berrado
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper reviews methods used to interpret random forest (RF) models, which are considered powerful learning ensembles due to their predictive performance, flexibility, and ease of use. However, the inner process of the RF model is complex, making it difficult to understand how the final decisions are made. The study provides a taxonomy of techniques for interpreting RF models, aiming to guide users in choosing suitable methods depending on the interpretability aspects sought. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at ways to make random forest models more understandable. These models are good at predicting things, but it’s hard to see how they make their decisions because there are many complicated trees inside them. The study shows different techniques people have used to make RF models easier to understand and explains how to choose the right one for a specific task. |
Keywords
» Artificial intelligence » Random forest
