Summary of Example-based Explanations For Random Forests Using Machine Unlearning, by Tanmay Surve and Romila Pradhan
Example-based Explanations for Random Forests using Machine Unlearning
by Tanmay Surve, Romila Pradhan
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 In this paper, researchers investigate why tree-based machine learning models, such as decision trees and random forests, sometimes produce unexpected or discriminatory outcomes despite their overall success in classification tasks. They explore sources of these issues to better understand and debug tree-based classifiers in the context of fairness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks into why some tree-based machine learning models make mistakes or show bias even though they are usually very good at predicting results. The researchers want to figure out what causes this problem so they can fix it and create fairer predictions. |
Keywords
* Artificial intelligence * Classification * Machine learning
