Optimal Sparse Survival Trees
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910456379080704 |
|---|---|
| author | Zhang, Rui Xin, Rui Seltzer, Margo Rudin, Cynthia |
| author_facet | Zhang, Rui Xin, Rui Seltzer, Margo Rudin, Cynthia |
| contents | Interpretability is crucial for doctors, hospitals, pharmaceutical companies and biotechnology corporations to analyze and make decisions for high stakes problems that involve human health. Tree-based methods have been widely adopted for survival analysis due to their appealing interpretablility and their ability to capture complex relationships. However, most existing methods to produce survival trees rely on heuristic (or greedy) algorithms, which risk producing sub-optimal models. We present a dynamic-programming-with-bounds approach that finds provably-optimal sparse survival tree models, frequently in only a few seconds. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15330 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimal Sparse Survival Trees Zhang, Rui Xin, Rui Seltzer, Margo Rudin, Cynthia Machine Learning Interpretability is crucial for doctors, hospitals, pharmaceutical companies and biotechnology corporations to analyze and make decisions for high stakes problems that involve human health. Tree-based methods have been widely adopted for survival analysis due to their appealing interpretablility and their ability to capture complex relationships. However, most existing methods to produce survival trees rely on heuristic (or greedy) algorithms, which risk producing sub-optimal models. We present a dynamic-programming-with-bounds approach that finds provably-optimal sparse survival tree models, frequently in only a few seconds. |
| title | Optimal Sparse Survival Trees |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.15330 |