Summary of Online Learning Of Decision Trees with Thompson Sampling, by Ayman Chaouki et al.
Online Learning of Decision Trees with Thompson Sampling
by Ayman Chaouki, Jesse Read, Albert Bifet
First submitted to arxiv on: 9 Apr 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 In this paper, researchers develop a new algorithm for building decision trees in an online setting, where data arrives in a stream. They introduce Thompson Sampling Decision Trees (TSDT), which produces optimal decision trees with guarantees of global optimality. This is a significant improvement over existing methods, which are often heuristic and may not produce the best possible tree. The authors prove that TSDT converges to the optimal tree almost surely, and conduct extensive experiments to validate their findings. Their algorithm outperforms existing methods on several benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Decision Trees are important tools for making predictions in machine learning. They help us understand how different factors contribute to an outcome. In the past, researchers have developed many algorithms for building Decision Trees, but these were mostly designed for use with a fixed set of data. This made them not very good at handling new information that comes in over time. The authors of this paper try to fix this problem by developing a new algorithm called Thompson Sampling Decision Trees (TSDT). It is designed to work well when you have a stream of new data coming in, and it can produce decision trees that are optimal for the task. |
Keywords
* Artificial intelligence * Machine learning
