Random generalized linear model: a highly accurate and interpretable ensemble predictor

BACKGROUND: Ensemble predictors such as the random forest are known to have superior accuracy but their black-box predictions are difficult to interpret. In contrast, a generalized linear model (GLM) is very interpretable especially when forward feature selection is used to construct the model. Howe...

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Detalles Bibliográficos
Autores principales: Song, Lin, Langfelder, Peter, Horvath, Steve
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3645958/
https://www.ncbi.nlm.nih.gov/pubmed/23323760
http://dx.doi.org/10.1186/1471-2105-14-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3645958/
https://www.ncbi.nlm.nih.gov/pubmed/23323760
http://dx.doi.org/10.1186/1471-2105-14-5

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