The behaviour of random forest permutation-based variable importance measures under predictor correlation.
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Different Statistical Approaches to Investigate Porcine Muscle Metabolome Profiles to Highlight New Biomarkers for Pork Quality AssessmentSplitting random forest (SRF) for determining compact sets of genes that distinguish between cancer subtypes.Combining techniques for screening and evaluating interaction terms on high-dimensional time-to-event data.A methodology for exploring biomarker--phenotype associations: application to flow cytometry data and systemic sclerosis clinical manifestationsAssessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining ApproachesOn safari to Random Jungle: a fast implementation of Random Forests for high-dimensional dataPredicting phenotypes of asthma and eczema with machine learning.Evidence for CRHR1 in multiple sclerosis using supervised machine learning and meta-analysis in 12,566 individuals.Gradient forests: calculating importance gradients on physical predictors.Random forests for genomic data analysis.PhenoLink--a web-tool for linking phenotype to ~omics data for bacteria: application to gene-trait matching for Lactobacillus plantarum strains.Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?Integrative analysis using module-guided random forests reveals correlated genetic factors related to mouse weight.An AUC-based permutation variable importance measure for random forestsLetter to the editor: on the stability and ranking of predictors from random forest variable importance measures.Exploiting SNP correlations within random forest for genome-wide association studiesLarge-Scale Examination of Spatio-Temporal Patterns of Drifting Fish Aggregating Devices (dFADs) from Tropical Tuna Fisheries of the Indian and Atlantic Oceans.The use of classification trees for bioinformatics.Prioritizing schizophrenia endophenotypes for future genetic studies: An example using data from the COGS-1 family study.Risk estimation and risk prediction using machine-learning methods.Intervention in prediction measure: a new approach to assessing variable importance for random forests.A systematic evaluation of nucleotide properties for CRISPR sgRNA design.Correction for population stratification in random forest analysisr2VIM: A new variable selection method for random forests in genome-wide association studies.Association of cerebrospinal fluid Aβ42 with A2M gene in cognitively normal subjects.Machine learning for predicting lifespan-extending chemical compoundsRAPIDSNPs: A new computational pipeline for rapidly identifying key genetic variants reveals previously unidentified SNPs that are significantly associated with individual platelet responsesImmunologic profiles distinguish aviremic HIV-infected adults.Predicting attention-deficit/hyperactivity disorder severity from psychosocial stress and stress-response genes: a random forest regression approachVariable selection method for the identification of epistatic models.Probability estimation with machine learning methods for dichotomous and multicategory outcome: applications.Root traits are more than analogues of leaf traits: the case for diaspore mass.Influence of hydrological regime and land cover on traits and potential export capacity of adult aquatic insects from river channels.Cost-Effectiveness of Peer- Versus Venue-Based Approaches for Detecting Undiagnosed HIV Among Heterosexuals in High-Risk New York City Neighborhoods.An automated framework for QSAR model building.Machine Learning and Radiogenomics: Lessons Learned and Future Directions.Direct and indirect effects of a pH gradient bring insights into the mechanisms driving prokaryotic community structures.Identifying appropriate spatial scales of predictors in species distribution models with the random forest algorithmMining data with random forests: current options for real-world applicationsOverview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics
P2860
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P2860
The behaviour of random forest permutation-based variable importance measures under predictor correlation.
description
2010 nî lūn-bûn
@nan
2010 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
The behaviour of random forest ...... s under predictor correlation.
@ast
The behaviour of random forest ...... s under predictor correlation.
@en
The behaviour of random forest ...... s under predictor correlation.
@nl
type
label
The behaviour of random forest ...... s under predictor correlation.
@ast
The behaviour of random forest ...... s under predictor correlation.
@en
The behaviour of random forest ...... s under predictor correlation.
@nl
prefLabel
The behaviour of random forest ...... s under predictor correlation.
@ast
The behaviour of random forest ...... s under predictor correlation.
@en
The behaviour of random forest ...... s under predictor correlation.
@nl
P2860
P356
P1433
P1476
The behaviour of random forest ...... s under predictor correlation.
@en
P2093
James D Malley
Kristin K Nicodemus
P2860
P2888
P356
10.1186/1471-2105-11-110
P577
2010-02-27T00:00:00Z