Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915259007107072 |
|---|---|
| author | Loffredo, Giuseppe Romano, Elvira MAturo, Fabrizio |
| author_facet | Loffredo, Giuseppe Romano, Elvira MAturo, Fabrizio |
| contents | Functional survival models are key tools for analyzing time-to-event data with complex predictors, such as functional or high-dimensional inputs. Despite their predictive strength, these models often lack interpretability, which limits their value in practical decision-making and risk analysis. This study investigates two key survival models: the Functional Survival Tree (FST) and the Functional Random Survival Forest (FRSF). It introduces novel methods and tools to enhance the interpretability of FST models and improve the explainability of FRSF ensembles. Using both real and simulated datasets, the results demonstrate that the proposed approaches yield efficient, easy-to-understand decision trees that accurately capture the underlying decision-making processes of the model ensemble. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18498 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests Loffredo, Giuseppe Romano, Elvira MAturo, Fabrizio Machine Learning Methodology 62N02, 62P10, 62H30, 62G05, 62G08, 62J99 G.3; I.5.1; I.5.2 Functional survival models are key tools for analyzing time-to-event data with complex predictors, such as functional or high-dimensional inputs. Despite their predictive strength, these models often lack interpretability, which limits their value in practical decision-making and risk analysis. This study investigates two key survival models: the Functional Survival Tree (FST) and the Functional Random Survival Forest (FRSF). It introduces novel methods and tools to enhance the interpretability of FST models and improve the explainability of FRSF ensembles. Using both real and simulated datasets, the results demonstrate that the proposed approaches yield efficient, easy-to-understand decision trees that accurately capture the underlying decision-making processes of the model ensemble. |
| title | Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests |
| topic | Machine Learning Methodology 62N02, 62P10, 62H30, 62G05, 62G08, 62J99 G.3; I.5.1; I.5.2 |
| url | https://arxiv.org/abs/2504.18498 |