Summary of Generating Global and Local Explanations For Tree-ensemble Learning Methods by Answer Set Programming, By Akihiro Takemura and Katsumi Inoue
Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programming
by Akihiro Takemura, Katsumi Inoue
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 proposed method uses Answer Set Programming (ASP) to generate rule sets as global and local explanations for tree-ensemble learning methods. The decompositional approach exploits the split structures of base decision trees, which are then assessed using pattern mining methods encoded in ASP to extract explanatory rules. Global explanations are derived from the entire trained model, while local explanations focus on relevant rules for a specific predicted instance. User-defined constraints and preferences can be represented declaratively in ASP for transparent and flexible rule set generation. The approach is demonstrated to be applicable to various classification tasks using real-world datasets and popular tree-ensemble algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machine learning models work by generating explanations in the form of rules. It uses a special programming language called Answer Set Programming (ASP) to create these rules, which can be used to explain why a model made a certain prediction. The approach is flexible and allows users to control what kinds of explanations are generated. This could be useful for people who want to understand how machine learning models work or want to use them in real-world applications. |
Keywords
» Artificial intelligence » Classification » Machine learning
