A simplified directional KeRF algorithm
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912268945457152 |
|---|---|
| author | Isidoros, Iakovidis Arcozzi, Nicola |
| author_facet | Isidoros, Iakovidis Arcozzi, Nicola |
| contents | Random forest methods belong to the class of non-parametric machine learning algorithms. They were first introduced in 2001 by Breiman and they perform with accuracy in high dimensional settings. In this article, we consider, a simplified kernel-based random forest algorithm called simplified directional KeRF (Kernel Random Forest). We establish the asymptotic equivalence between simplified directional KeRF and centered KeRF, with additional numerical experiments supporting our theoretical results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04042 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A simplified directional KeRF algorithm Isidoros, Iakovidis Arcozzi, Nicola Statistics Theory Primary 62G05, Secondary 62G20, 46E22, 43A75 Random forest methods belong to the class of non-parametric machine learning algorithms. They were first introduced in 2001 by Breiman and they perform with accuracy in high dimensional settings. In this article, we consider, a simplified kernel-based random forest algorithm called simplified directional KeRF (Kernel Random Forest). We establish the asymptotic equivalence between simplified directional KeRF and centered KeRF, with additional numerical experiments supporting our theoretical results. |
| title | A simplified directional KeRF algorithm |
| topic | Statistics Theory Primary 62G05, Secondary 62G20, 46E22, 43A75 |
| url | https://arxiv.org/abs/2407.04042 |