Summary of Learning Decision Trees and Forests with Algorithmic Recourse, by Kentaro Kanamori et al.
Learning Decision Trees and Forests with Algorithmic Recourse
by Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper proposes Algorithmic Recourse (AR), an algorithm for learning accurate tree-based models that ensures the existence of recourse actions, which are actions that can alter undesired prediction results. The traditional AR methods provide a reasonable action by minimizing effort among executable actions, but this doesn’t always exist when models are optimized only for predictive performance. To address this, the paper formulates the task of learning an accurate classification tree under the constraint of ensuring the existence of reasonable actions for as many instances as possible. A top-down greedy algorithm is proposed, leveraging adversarial training techniques, and shown to be applicable to random forest, a popular framework for learning tree ensembles. Experimental results demonstrate that the method successfully provides reasonable actions to more instances than baselines without significantly degrading accuracy or computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how we can make machines learn in a way that allows them to change their decisions if they’re wrong. This is important because sometimes, these machines can make mistakes and it’s good to have ways to correct those mistakes. The researchers created a new algorithm called Algorithmic Recourse (AR) that makes sure the machine can take actions to fix its mistakes. They tested this on some popular computer programs and found that their method worked well without hurting the machine’s ability to make accurate predictions. |
Keywords
» Artificial intelligence » Classification » Random forest
