ArticleBMC bioinformatics2020
Conditional permutation importance revisited.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Tutorial: Using Random Forest Analysis to Identify Auxiliary Variables of Missing Data.Prevention science : the official journal of the Society for Prevention Research · 2026Article
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- Downscaling local distribution of cattle over Guadeloupe archipelago: An adapted method for disaggregating census data.PloS one · 2026Article
- Article
- Global ecological associations between overweight/obesity and non-communicable disease prevalence among children aged 5-9 years: a sex-stratified analysis of 174 countries.Frontiers in public health · 2026Article
- Interpreting Supervised Machine Learning Inferences in Population Genomics Using Haplotype Matrix Permutations.Molecular biology and evolution · 2025Article
- Coupling Genetic and Demographic Data to Reveal Dispersal Processes in Emperor Penguins.Ecology and evolution · 2025Article
- Multiple, Single Trait GWAS and Supervised Machine Learning Reveal the Genetic Architecture of Fraxinus excelsior Tolerance to Ash Dieback in Europe.Plant, cell & environment · 2025Article
- Variable Importance Measures Suggest Paramount Influence of Human Economics on Alien-Species Introductions.Ecology and evolution · 2025Article
- Stochastic modeling of economic risk and net return distributions for feedlot steers marketed at alternative endpoints.Journal of animal science · 2025Article
- Enhancing drug repositioning: A multi-class ensemble model for drug-target interaction prediction with action type categorization.PloS one · 2025Article
- Machine learning for identifying caregiving adversities associated with greatest risk for mental health problems in children.Nature. Mental health · 2025Article
- Random forest analysis and lasso regression outperform traditional methods in identifying missing data auxiliary variables when the MAR mechanism is nonlinear (p.s. Stop using Little's MCAR test).Behavior research methods · 2024Article
- Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States.eLife · 2024Article
- Hydrologic responses to wildfires in western Oregon, USA.Journal of hydrology · 2024Article
- Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States.medRxiv : the preprint server for health sciences · 2024Article
- Predicting pediatric severe asthma exacerbations: an administrative claims-based predictive model.The Journal of asthma : official journal of the Association for the Care of Asthma · 2024Article
- Go with the flow: Impacts of high and low flow conditions on freshwater mussel assemblages and distribution.PloS one · 2024Article
- Exploring the variable importance in random forests under correlations: a general concept applied to donor organ quality in post-transplant survival.BMC medical research methodology · 2023Article
- Preoperative Prediction and Risk Factor Identification of Hospital Length of Stay for Total Joint Arthroplasty Patients Using Machine Learning.Arthroplasty today · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRandom forest based variable importance measures have become popular tools for assessing the contributions of the predictor variables in a fitted random forest. In this article we reconsider a frequently used variable importance measure, the Conditional Permutation Importance (CPI). We argue and illustrate that the CPI corresponds to a more partial quantification of variable importance and suggest several improvements in its methodology and implementation that enhance its practical value. In addition, we introduce the threshold value in the CPI algorithm as a parameter that can make the CPI more partial or more marginal.
resultsBy means of extensive simulations, where the original version of the CPI is used as the reference, we examine the impact of the proposed methodological improvements. The simulation results show how the improved CPI methodology increases the interpretability and stability of the computations. In addition, the newly proposed implementation decreases the computation times drastically and is more widely applicable. The improved CPI algorithm is made freely available as an add-on package to the open-source software R.
conclusionThe proposed methodology and implementation of the CPI is computationally faster and leads to more stable results. It has a beneficial impact on practical research by making random forest analyses more interpretable.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.