Summary of Branches: Efficiently Seeking Optimal Sparse Decision Trees with Ao*, by Ayman Chaouki et al.
Branches: Efficiently Seeking Optimal Sparse Decision Trees with AO*
by Ayman Chaouki, Jesse Read, Albert Bifet
First submitted to arxiv on: 4 Jun 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 This paper tackles the fundamental problem of Decision Tree (DT) Learning in Interpretable Machine Learning, a challenging optimisation task. Recent algorithms have emerged, leveraging Dynamic Programming and Branch & Bound, but they rely on Depth-First-Search strategies, which are inefficient at high depths and require setting a maximum depth hyperparameter. To overcome this limitation, the Best-First-Search strategy was employed by other methods, but it comes with higher memory consumption. The authors formulate the problem as an AND/OR graph search and propose a novel AO*-type algorithm called Branches, which offers optimality and complexity guarantees. Compared to state-of-the-art methods, Branches is more efficient theoretically and experimentally. Additionally, Branches supports non-binary features, leading to larger gains in computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on making Decision Trees (a type of machine learning) work better and faster. Right now, there are some algorithms that try to make this happen, but they have limitations. One problem is that they get stuck searching through too many possibilities, which makes them slow. The authors came up with a new way to search for the best Decision Tree, called Branches. It’s more efficient than previous methods and can handle features that are not just yes or no (binary). This means we can get better results with less effort. |
Keywords
» Artificial intelligence » Decision tree » Hyperparameter » Machine learning
