survex: an R package for explaining machine learning survival models
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929493414772736 |
|---|---|
| author | Spytek, Mikołaj Krzyziński, Mateusz Langbein, Sophie Hanna Baniecki, Hubert Wright, Marvin N. Biecek, Przemysław |
| author_facet | Spytek, Mikołaj Krzyziński, Mateusz Langbein, Sophie Hanna Baniecki, Hubert Wright, Marvin N. Biecek, Przemysław |
| contents | Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread adoption is hindered by a lack of user-friendly tools to explain their internal operations and prediction rationales. To tackle this issue, we introduce the survex R package, which provides a cohesive framework for explaining any survival model by applying explainable artificial intelligence techniques. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement. By revealing insights into the decision-making process, such as variable effects and importances, survex enables the assessment of model reliability and the detection of biases. Thus, transparency and responsibility may be promoted in sensitive areas, such as biomedical research and healthcare applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_16113 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | survex: an R package for explaining machine learning survival models Spytek, Mikołaj Krzyziński, Mateusz Langbein, Sophie Hanna Baniecki, Hubert Wright, Marvin N. Biecek, Przemysław Machine Learning Artificial Intelligence Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread adoption is hindered by a lack of user-friendly tools to explain their internal operations and prediction rationales. To tackle this issue, we introduce the survex R package, which provides a cohesive framework for explaining any survival model by applying explainable artificial intelligence techniques. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement. By revealing insights into the decision-making process, such as variable effects and importances, survex enables the assessment of model reliability and the detection of biases. Thus, transparency and responsibility may be promoted in sensitive areas, such as biomedical research and healthcare applications. |
| title | survex: an R package for explaining machine learning survival models |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2308.16113 |