Summary of Optimal Sparse Survival Trees, by Rui Zhang et al.
Optimal Sparse Survival Trees
by Rui Zhang, Rui Xin, Margo Seltzer, Cynthia Rudin
First submitted to arxiv on: 27 Jan 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 proposes a novel method for building interpretable survival trees using dynamic programming with bounds. Tree-based methods are widely used in medicine and biotechnology to analyze complex relationships and make high-stakes decisions. However, most existing approaches rely on heuristic algorithms that may produce sub-optimal models. The proposed approach finds provably-optimal sparse survival tree models quickly, often in just a few seconds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Doctors, hospitals, pharmaceutical companies, and biotech corporations need to analyze data and make decisions about human health. Survival trees are useful for this because they can show complex relationships and be understood easily. But most methods use “greedy” algorithms that might not find the best model. This paper presents a new way to build survival trees using dynamic programming with bounds, which finds the best models quickly. |
