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

By Anne-Laure Boulesteix, Silke Janitza, Jochen Kruppa, Inke R. König. WIREs Data Mining Knowl. Discov., 2012

896 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b779b41898931373f6e1f302b0a0d9a38313956a

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Citations in the corpus

Understanding Random Forests: From Theory to Practice (2014, background): “ of further studies based on random forestscouldinfactbecitedfromtheeldsofgenomics,metabolomics, proteomics or transcriptomics. Some of them are reviewed in [Geurts et al., 2009 ; Touw et al., 2013 ; Boulesteix et al., 2012 ]. In light of our study and discussion of variable importances, recommendat”
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources (2019, methodology): “The latter rank the predictor variables in terms of their relative significance, but provide limited information regarding the absolute performance of individual predictors in modeling the response variables [16].”
Random forests: from early developments to recent advancements (2014, background): “In Bioinformatics and Computational Biology , Boulesteix, Janitza, Kruppa, and König (2012) amalga-mated 10 years of RF development.”
Neural Random Forests (2016, background): “The surveys by Boulesteix et al. (2012) and Biau and Scornet (2016), which include practical guidelines and updated references, are a good starting point for understanding the method.”
Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas (2016, result): “These results are also consistent with Cutler et al. (2007) and Boulesteix et al. (2012): they asserted that it is preferable to tune m for each case even if accuracy gain is weak.”
VSURF: An R Package for Variable Selection Using Random Forests (2015, neutral): “The RF algorithm is a very popular machine learning algorithm and appears to be powerful in a lot of different applications, see for example Verikas et al. (2011) and Boulesteix et al. (2012) for recent surveys.”
How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset (2018, background): “In this case, there should be some compromise between all options (Boulesteix et al. 2012).”
Random forest versus logistic regression: a large-scale benchmark experiment (2018, background): “Note, however, that alternative choices may yield better performance [15, 16] and that parameter tuning for RF has to be further addressed in future research.”