Cargando…
Variable importance for sustaining macrophyte presence via random forests: data imputation and model settings
Data sets plagued with missing data and performance-affecting model parameters represent recurrent issues within the field of data mining. Via random forests, the influence of data reduction, outlier and correlated variable removal and missing data imputation technique on the performance of habitat...
Autores principales: | Van Echelpoel, Wout, Goethals, Peter L. M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6162213/ https://www.ncbi.nlm.nih.gov/pubmed/30266931 http://dx.doi.org/10.1038/s41598-018-32966-2 |
Ejemplares similares
-
Functional Response (FR) and Relative Growth Rate (RGR) Do Not Show the Known Invasiveness of Lemna minuta (Kunth)
por: Van Echelpoel, Wout, et al.
Publicado: (2016) -
Sampling errors and variability in video transects for assessment of reef fish assemblage structure and diversity
por: Bruneel, Stijn, et al.
Publicado: (2022) -
Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction
por: Hong, Shangzhi, et al.
Publicado: (2020) -
Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study
por: Shah, Anoop D., et al.
Publicado: (2014) -
Conditional variable importance for random forests
por: Strobl, Carolin, et al.
Publicado: (2008)