Cargando…
The parameter sensitivity of random forests
BACKGROUND: The Random Forest (RF) algorithm for supervised machine learning is an ensemble learning method widely used in science and many other fields. Its popularity has been increasing, but relatively few studies address the parameter selection process: a critical step in model fitting. Due to n...
Autores principales: | Huang, Barbara F.F., Boutros, Paul C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009551/ https://www.ncbi.nlm.nih.gov/pubmed/27586051 http://dx.doi.org/10.1186/s12859-016-1228-x |
Ejemplares similares
-
Cluster ensemble based on Random Forests for genetic data
por: Alhusain, Luluah, et al.
Publicado: (2017) -
Block Forests: random forests for blocks of clinical and omics covariate data
por: Hornung, Roman, et al.
Publicado: (2019) -
Genome-scale CRISPR screening at high sensitivity with an empirically designed sgRNA library
por: Henkel, Luisa, et al.
Publicado: (2020) -
Conditional variable importance for random forests
por: Strobl, Carolin, et al.
Publicado: (2008) -
Detecting gene-gene interactions using a permutation-based random forest method
por: Li, Jing, et al.
Publicado: (2016)