Cargando…

Conditional variable importance for random forests

BACKGROUND: Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been suggested as screening to...

Descripción completa

Detalles Bibliográficos
Autores principales:	Strobl, Carolin, Boulesteix, Anne-Laure, Kneib, Thomas, Augustin, Thomas, Zeileis, Achim
Formato:	Texto
Lenguaje:	English
Publicado:	BioMed Central 2008
Materias:	Methodology Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2491635/ https://www.ncbi.nlm.nih.gov/pubmed/18620558 http://dx.doi.org/10.1186/1471-2105-9-307

Ejemplares similares

Bias in random forest variable importance measures: Illustrations, sources and a solution
por: Strobl, Carolin, et al.
Publicado: (2007)

An AUC-based permutation variable importance measure for random forests
por: Janitza, Silke, et al.
Publicado: (2013)

Conditional permutation importance revisited
por: Debeer, Dries, et al.
Publicado: (2020)

Letter to the Editor: On the term ‘interaction’ and related phrases in the literature on Random Forests
por: Boulesteix, Anne-Laure, et al.
Publicado: (2015)

The behaviour of random forest permutation-based variable importance measures under predictor correlation
por: Nicodemus, Kristin K, et al.
Publicado: (2010)

An R toolbox for score-based measurement invariance tests in IRT models
por: Schneider, Lennart, et al.
Publicado: (2021)

Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction
por: Boulesteix, Anne-Laure, et al.
Publicado: (2009)

Intervention in prediction measure: a new approach to assessing variable importance for random forests
por: Epifanio, Irene
Publicado: (2017)

Publication Bias in Methodological Computational Research
por: Boulesteix, Anne-Laure, et al.
Publicado: (2015)

Anchor Point Selection: Scale Alignment Based on an Inequality Criterion
por: Strobl, Carolin, et al.
Publicado: (2021)

Testing the additional predictive value of high-dimensional molecular data
por: Boulesteix, Anne-Laure, et al.
Publicado: (2010)

r2VIM: A new variable selection method for random forests in genome-wide association studies
por: Szymczak, Silke, et al.
Publicado: (2016)

Random forest versus logistic regression: a large-scale benchmark experiment
por: Couronné, Raphael, et al.
Publicado: (2018)

Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment
por: Hornung, Roman, et al.
Publicado: (2016)

Sigma-RF: prediction of the variability of spatial restraints in template-based modeling by random forest
por: Lee, Juyong, et al.
Publicado: (2015)

Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data
por: Klau, Simon, et al.
Publicado: (2018)

Cluster ensemble based on Random Forests for genetic data
por: Alhusain, Luluah, et al.
Publicado: (2017)

Variable selection strategies and its importance in clinical prediction modelling
por: Chowdhury, Mohammad Ziaul Islam, et al.
Publicado: (2020)

The parameter sensitivity of random forests
por: Huang, Barbara F.F., et al.
Publicado: (2016)

Block Forests: random forests for blocks of clinical and omics covariate data
por: Hornung, Roman, et al.
Publicado: (2019)

Comparison of calculation methods for estimating annual carbon stock change in German forests under forest management in the German greenhouse gas inventory
por: Röhling, Steffi, et al.
Publicado: (2016)

Detecting gene-gene interactions using a permutation-based random forest method
por: Li, Jing, et al.
Publicado: (2016)

PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets
por: Pradier, Léa, et al.
Publicado: (2021)

Surrogate minimal depth as an importance measure for variables in random forests
por: Seifert, Stephan, et al.
Publicado: (2019)

Evaluating Microarray-based Classifiers: An Overview
por: Boulesteix, A.-L., et al.
Publicado: (2008)

Splitting random forest (SRF) for determining compact sets of genes that distinguish between cancer subtypes
por: Guan, Xiaowei, et al.
Publicado: (2012)

Prediction of donor splice sites using random forest with a new sequence encoding approach
por: Meher, Prabina Kumar, et al.
Publicado: (2016)

Use of allele scores as instrumental variables for Mendelian randomization
por: Burgess, Stephen, et al.
Publicado: (2013)

Importance of presenting the variability of the false discovery rate control
por: Lin, Yi-Ting, et al.
Publicado: (2015)

Performance of random forest when SNPs are in linkage disequilibrium
por: Meng, Yan A, et al.
Publicado: (2009)

Random KNN feature selection - a fast and stable alternative to Random Forests
por: Li, Shengqiao, et al.
Publicado: (2011)

An experimental study of the intrinsic stability of random forest variable importance measures
por: Wang, Huazhen, et al.
Publicado: (2016)

Reliable measurement of plasma kinin peptides: Importance of preanalytical variables
por: Gangnus, Tanja, et al.
Publicado: (2022)

An evaluation of transport mode shift policies on transport-related physical activity through simulations based on random forests
por: Brondeel, Ruben, et al.
Publicado: (2017)

iPseU-NCP: Identifying RNA pseudouridine sites using random forest and NCP-encoded features
por: Nguyen-Vo, Thanh-Hoang, et al.
Publicado: (2019)

A method combining a random forest-based technique with the modeling of linkage disequilibrium through latent variables, to run multilocus genome-wide association studies
por: Sinoquet, Christine
Publicado: (2018)

Gene selection and classification of microarray data using random forest
por: Díaz-Uriarte, Ramón, et al.
Publicado: (2006)

Improved high-dimensional prediction with Random Forests by the use of co-data
por: te Beest, Dennis E., et al.
Publicado: (2017)

Accurate prediction of metagenome-assembled genome completeness by MAGISTA, a random forest model built on alignment-free intra-bin statistics
por: Goussarov, Gleb, et al.
Publicado: (2022)

Dimension reduction with gene expression data using targeted variable importance measurement
por: Wang, Hui, et al.
Publicado: (2011)

Cannot write session to /tmp/vufind_sessions/sess_gdh9v9b80cpa6btoininji4per