ggRandomForests: Visually Exploring Random Forests

ggRandomForests provides ggplot2 (Wickham 2009) diagnostic and exploration figures for random forests grown with rfsrc (>= 3.4.0) or randomForest.

randomForestSRC gives a unified treatment of Breiman's (2001) random forests across data settings: regression and classification forests when the response is numeric or categorical, survival and competing-risk forests (Ishwaran et al. 2008) for right-censored data.

Details

The package is built on one decision: keep the data step and the figure step apart. A gg_* function pulls a tidy data object out of the forest; its plot() method turns that object into a figure. Two things follow.

The data object stands on its own. It carries everything its plot needs, so you can save it, inspect it, or come back to it later without keeping the original forest – which can be large – in memory.

You are never locked into the default figure. Each plot() method returns a single plottable object: a ggplot you extend with +, or a patchwork composite for the multi-panel methods. Add layers, swap scales, apply a theme – or ignore the default entirely and build the figure from the tidy data yourself. Every gg_* object also carries print() and summary() methods: print() shows a short header rather than dumping every row, and summary() returns a diagnostics object.

Forest diagnostics

• gg_rfsrc: predicted versus observed values.

• gg_error: OOB error against the number of trees.

• gg_vimp: variable importance ranking (Ishwaran et al. 2010).

• gg_variable: marginal variable dependence.

• gg_roc: ROC curves for classification forests (see also calc_roc and calc_auc).

• gg_survival: Kaplan-Meier / Nelson-Aalen estimates.

• gg_brier: time-resolved Brier score and CRPS for survival forests.

Partial dependence

• gg_partial: tidies the output of randomForestSRC::plot.variable(partial = TRUE).

• gg_partial_rfsrc: computes partial dependence from the fitted forest directly, via partial.rfsrc.

SHAP explanations

• gg_shap: SHAP values for regression and classification forests, wrapping kernelshap, with shap_importance, shap_beeswarm and shap_dependence figures.

varPro rule-based variable selection

• gg_varpro: variable importance from a varpro fit.

• gg_partial_varpro (alias gg_partialpro): partial effects on an interpretable scale.

• gg_beta_varpro: per-region lasso coefficients.

• gg_isopro: isolation-forest outlier scores.

• gg_ivarpro: individual (per-observation) importance.

Unsupervised varPro

• gg_udependent: variable dependency graph.

• gg_beta_uvarpro: entropy-based variable ranking.

• gg_sdependent: signal-variable detection.

varPro is a required dependency (Imports), so the varPro families are always available. kernelshap is in Suggests: gg_shap checks for it and fails with a clear message when it is not installed.

References

Breiman, L. (2001). Random forests, Machine Learning, 45:5-32.

Ishwaran H. and Kogalur U.B. randomForestSRC: Random Forests for Survival, Regression and Classification. R package version >= 3.4.0. https://cran.r-project.org/package=randomForestSRC

Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R. R News 7(2), 25–31.

Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests. Ann. Appl. Statist. 2(3), 841–860.

Ishwaran, H., U. B. Kogalur, E. Z. Gorodeski, A. J. Minn, and M. S. Lauer (2010). High-dimensional variable selection for survival data. J. Amer. Statist. Assoc. 105, 205-217.

Ishwaran, H. (2007). Variable importance in binary regression trees and forests. Electronic J. Statist., 1, 519-537.

Wickham, H. ggplot2: elegant graphics for data analysis. Springer New York, 2009.