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Hyperparameters and tuning strategies for random forest

Wiley interdisciplinary reviews. Data mining and knowledge discovery, 2019-05, Vol.9 (3), p.e1301-n/a

2019

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Random forest versus logistic regression: a large-scale benchmark experiment

BMC bioinformatics, 2018-07, Vol.19 (1), p.270-270, Article 270

2018

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Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges

Wiley interdisciplinary reviews. Data mining and knowledge discovery, 2023-03, Vol.13 (2), p.e1484-n/a

2023

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Validation of cluster analysis results on validation data: A systematic framework

Wiley interdisciplinary reviews. Data mining and knowledge discovery, 2022-05, Vol.12 (3), p.e1444-n/a

2022

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NetCoMi: network construction and comparison for microbiome data in R

Briefings in bioinformatics, 2021-07, Vol.22 (4)

2021

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Phases of methodological research in biostatistics—Building the evidence base for new methods

Biometrical journal, 2024-01, Vol.66 (1), p.e2200222-n/a

2024

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Random forest for ordinal responses: Prediction and variable selection

Computational statistics & data analysis, 2016-04, Vol.96, p.57-73

2016

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Conditional variable importance for random forests

BMC bioinformatics, 2008-07, Vol.9 (1), p.307-307, Article 307

2008

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On the necessity and design of studies comparing statistical methods

Biometrical journal, 2018-01, Vol.60 (1), p.216-218

2018

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Ten simple rules for reducing overoptimistic reporting in methodological computational research

PLoS computational biology, 2015-04, Vol.11 (4), p.e1004191-e1004191

2015

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Explaining the optimistic performance evaluation of newly proposed methods: A cross‐design validation experiment

Biometrical journal, 2024-01, Vol.66 (1), p.e2200238-n/a

2024

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Subsampling versus bootstrapping in resampling-based model selection for multivariable regression

Biometrics, 2016-03, Vol.72 (1), p.272-280

2016

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Bias in random forest variable importance measures: illustrations, sources and a solution

BMC bioinformatics, 2007-01, Vol.8 (1), p.25-25, Article 25

2007

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Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Wiley interdisciplinary reviews. Data mining and knowledge discovery, 2012-11, Vol.2 (6), p.493-507

2012

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Essential guidelines for computational method benchmarking

Genome Biology, 2019-06, Vol.20 (1), p.125-125, Article 125

2019

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A comparison of hyperparameter tuning procedures for clinical prediction models: A simulation study

Statistics in medicine, 2024-03, Vol.43 (6), p.1119-1134

2024

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Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease

European heart journal, 2022-08, Vol.43 (31), p.2921-2930

2022

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Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data

BMC bioinformatics, 2018-09, Vol.19 (1), p.322-322, Article 322

2018

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Prediction approaches for partly missing multi‐omics covariate data: A literature review and an empirical comparison study

Wiley interdisciplinary reviews. Computational statistics, 2024-01, Vol.16 (1), p.e1626-n/a

2024

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Towards evidence-based computational statistics: lessons from clinical research on the role and design of real-data benchmark studies

BMC medical research methodology, 2017-09, Vol.17 (1), p.138-138, Article 138

2017

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Editorial for the special collection “Towards neutral comparison studies in methodological research”

Biometrical journal, 2024-03, Vol.66 (2), p.e2400031-n/a

2024

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Investigating the prediction ability of survival models based on both clinical and omics data: two case studies

Statistics in medicine, 2014-12, Vol.33 (30), p.5310-5329

2014

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Over‐optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results

Wiley interdisciplinary reviews. Data mining and knowledge discovery, 2022-03, Vol.12 (2), p.e1441-n/a

2022

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On the optimistic performance evaluation of newly introduced bioinformatic methods

Genome Biology, 2021-05, Vol.22 (1), p.152-8, Article 152

2021

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Regularized estimation of large-scale gene association networks using graphical Gaussian models

BMC bioinformatics, 2009-11, Vol.10 (1), p.384-384, Article 384

2009

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Hyperparameters and tuning strategies for random forest

Random forest versus logistic regression: a large-scale benchmark experiment

Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges

Validation of cluster analysis results on validation data: A systematic framework

NetCoMi: network construction and comparison for microbiome data in R

Phases of methodological research in biostatistics—Building the evidence base for new methods

Random forest for ordinal responses: Prediction and variable selection

Conditional variable importance for random forests

On the necessity and design of studies comparing statistical methods

Ten simple rules for reducing overoptimistic reporting in methodological computational research

Explaining the optimistic performance evaluation of newly proposed methods: A cross‐design validation experiment

Subsampling versus bootstrapping in resampling-based model selection for multivariable regression

Bias in random forest variable importance measures: illustrations, sources and a solution

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

Essential guidelines for computational method benchmarking

A comparison of hyperparameter tuning procedures for clinical prediction models: A simulation study

Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease

Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data

Prediction approaches for partly missing multi‐omics covariate data: A literature review and an empirical comparison study

Towards evidence-based computational statistics: lessons from clinical research on the role and design of real-data benchmark studies

Editorial for the special collection “Towards neutral comparison studies in methodological research”

Investigating the prediction ability of survival models based on both clinical and omics data: two case studies

Over‐optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results

On the optimistic performance evaluation of newly introduced bioinformatic methods

Regularized estimation of large-scale gene association networks using graphical Gaussian models

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