Summary of Case-based Explainability For Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals, by Gregory Yampolsky et al.
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals
by Gregory Yampolsky, Dhruv Desai, Mingshu Li, Stefano Pasquali, Dhagash Mehta
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistical Finance (q-fin.ST); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel approach to Explainable Case-Based Reasoning (XCBR) for tree-based models, specifically Random Forests (RFs), by extracting the distance metric learned by RFs. This technique is geometry- and accuracy-preserving, allowing various XCBR methods to be investigated. These methods involve identifying special points from the training datasets, such as prototypes, critics, counter-factuals, and semi-factuals, to explain predictions for a given query of the RF. The effectiveness and explanatory power of these special points are evaluated using various evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making machine learning algorithms more understandable so that they can be used in financial and other regulated industries where transparency is important. It proposes a new way to do this called Explainable Case-Based Reasoning (XCBR) for tree-based models like Random Forests. This method helps explain why the model made certain predictions by looking at specific examples from the data it was trained on. The researchers test their approach using different methods and evaluate how well they work. |
Keywords
» Artificial intelligence » Machine learning
