{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ggrandomforests-visually-exploring-a-random","title":"ggRandomForests: Visually Exploring a Random Forest for Regression","arxiv_id":"1501.07196","date":"2015-01-28","proceeding":null,"authors":["John Ehrlinger"],"abstract":"Random Forests [Breiman:2001] (RF) are a fully non-parametric statistical\nmethod requiring no distributional assumptions on covariate relation to the\nresponse. RF are a robust, nonlinear technique that optimizes predictive\naccuracy by fitting an ensemble of trees to stabilize model estimates. The\nrandomForestSRC package (http://cran.r-project.org/package=randomForestSRC) is\na unified treatment of Breiman's random forests for survival, regression and\nclassification problems. Predictive accuracy make RF an attractive alternative\nto parametric models, though complexity and interpretability of the forest\nhinder wider application of the method. We introduce the ggRandomForests\npackage (http://cran.r-project.org/package=ggRandomForests), for visually\nunderstand random forest models grown in R with the randomForestSRC package.\n  The vignette is a tutorial for using the ggRandomForests package with the\nrandomForestSRC package for building and post-processing a regression random\nforest. In this tutorial, we explore a random forest model for the Boston\nHousing Data, available in the MASS package. We grow a random forest for\nregression and demonstrate how ggRandomForests can be used when determining\nvariable associations, interactions and how the response depends on predictive\nvariables within the model. The tutorial demonstrates the design and usage of\nmany of ggRandomForests functions and features how to modify and customize the\nresulting ggplot2 graphic objects along the way.\n  A development version of the ggRandomForests package is available on Github.\nWe invite comments, feature requests and bug reports for this package at\n(https://github.com/ehrlinger/ggRandomForests).","url_abs":"http://arxiv.org/abs/1501.07196v2","url_pdf":"http://arxiv.org/pdf/1501.07196v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ggrandomforests-visually-exploring-a-random","repo_url":"https://github.com/ehrlinger/ggRandomForests","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}