ggRandomForests: Exploring Random Forest Survival
John Ehrlinger

TL;DR
This paper introduces the ggRandomForests package for visualizing and understanding random forest models applied to survival analysis, demonstrating its use with clinical data to enhance interpretability and predictive insights.
Contribution
The paper presents a new R package, ggRandomForests, that facilitates visualization and interpretation of random forest models for survival data, complementing the existing randomForestSRC package.
Findings
Effective visualization of survival forests using ggplot2
Application to clinical liver disease data
Enhanced interpretability of complex models
Abstract
Random forest (Leo Breiman 2001a) (RF) is a non-parametric statistical method requiring no distributional assumptions on covariate relation to the response. RF is a robust, nonlinear technique that optimizes predictive accuracy by fitting an ensemble of trees to stabilize model estimates. Random survival forests (RSF) (Ishwaran and Kogalur 2007; Ishwaran et al. 2008) are an extension of Breimans RF techniques allowing efficient nonparametric analysis of time to event data. The randomForestSRC package (Ishwaran and Kogalur 2014) is a unified treatment of Breimans random forest for survival, regression and classification problems. Predictive accuracy makes RF an attractive alternative to parametric models, though complexity and interpretability of the forest hinder wider application of the method. We introduce the ggRandomForests package, tools for visually understand random forest…
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Taxonomy
TopicsGenetics and Plant Breeding · Statistical Methods and Inference · Advanced Statistical Methods and Models
MethodsInterpretability
