The Arborist project hosts fast, open-source implementations of several decision-tree algorithms. Breiman and Cutler's Random Forest algorithm is implemented and Friedman's Stochastic Gradient Boosting is available as an alpha release. A spin providing Friedman and Fisher's PRIM ("Patient Rule Induction Method") has been developed by Decision Patterns, LLC. Arborist derivatives achieve their speed through parallelized and vectorized inner loops. Parallel, distributed training is also possible for independently-trained trees. Considerable attention has been devoted to minimizing and regularizing data movement, a key challenge to accelerating these algorithms.

Bindings are provided for R. A language-agnostic bridge design supports development of bindings for additional front ends, such as Python and Julia.

CRAN hosts the released package Rborist, which implements the Random Forest algorithm.

Installation of the released version using R:

> install.packages('Rborist')

Installation of the development version, hosted on this archive, from the top-level directory:

> ./Rborist/Package/Rborist.CRAN.sh
> R CMD INSTALL Rborist_*.*-*.tar.gz

A CRAN-friendly snapshot of the development source is mirrored by the neighboring archive Rborist.CRAN. This archive is intended for remote access by R utilities such as devtools.

• Rborist version 0.3-7 is hosted on CRAN.

• Version 0.1-0 has been archived.
• Test cases sought.

Performance metrics have been measured using benchm-ml. Partial results can be found here

Some users have reported diminished performance when running single-threaded. We recommend running with at least two cores, as frequently-executed inner loops have been cast specifically to take advantage of multiple cores. In particular, when using a scaffold such as caret, please prefer to let Rborist be greedier with cores than is the scaffold.

In a 2015 paper, https://www.jstatsoft.org/article/view/v077i01/v77i01.pdf, Wright and Ziegler compare several R-language implementations of the Random Forest algorithm, including Ranger and Rborist. A key finding was that Rborist failed to achieve the $O(\sqrt{p})$ scaling expected for classification under high predictor (feature) count, p. Although the default behavior for classification is to select $\sqrt{p}$ splitting candidates at a given node, the repartitioning scheme then in use eclipsed any benefit of restricting to such a sparse candidate set. Repartitioning had been implemented greedily, taking place on every node/predictor cell regardless whether a candidate. As a result of the authors' findings, repartitioning was revised the following year to employ a lazy scheme, only applied to cells either about to undergo splitting or overflowing a fixed-size deque. Using an 8-bit repartitioning history, the lazy scheme appears to scale appropriately beyond p = $10^4$. A 16-bit history can be substituted to bring scaling well beyond p = $10^6$. Although not explicitly stated, their work illustrates that data movement (in this case, repartitioning) poses a greater challenge to scaling than does splitting. Benchmarks used in the study are provided alongside the paper at https://www.jstatsoft.org/article/view/v077i01.

A 2019 paper compares several categories of regression tools, including Random Forests. Rborist is among the faster packages offering high prediction accuracy: (https://doi.org/10.1109/ACCESS.2019.2933261). Based on the findings, we have updated the package's default settings. In particular, fixed-number predictor sampling (mtry) appears to provide more accurate predictions at low dimension than the current approach of Bernoulli sampling.

• Scalability Issues in Training Decision Trees (video), Nimbix Developer Summit, 2017.
• Controlling for Monotonicity in Random Forest Regressors (PDF), R in Finance, May 2016.
• Accelerating the Random Forest algorithm for commodity parallel hardware (Video), PyData, July, 2015.
• The Arborist: A High-Performance Random Forest (TM) Implementation, R in Finance, May 2015.
• Training Random Forests on the GPU: Tree Unrolling (PDF), GTC, March, 2015.

• New archive sgbArb.CRAN mirrors the sgbArb package for stochastic gradient boosting.
• New archive Rborist.CRAN mirrors the Rborist package source in a form directly amenable to utilities such as devtools.
• New command rfTrain exposes the training component of the compound format/sample/train/validate task performed by rfArb. This provides separate training of sampled, prefomatted data.
• New prediction option keyedFrame accesses prediction columns by name, bypassing a previous requirement that training and prediction frames have the same column ordering. In addition to arbitrary ordering, the prediction frame may now include columns not submitted to training.
• New command forestWeight computes Meinshausen's forest-wide weights. Nonterminals are weighted in addition to leaves, both to facilitate post-pruning and to accommodate early exit under prediction with trap-and-bail.
• New prediction option indexing=TRUE records final node indices of tree walks.
• Training ignores missing predictor values, splitting over appropriately reduced subnodes.
• Quantile estimation supports both leaf and nonterminal (i.e., trap-and-bail) prediction modes.
• Prediction and validiation support large (> 32 bits) observation counts.
• Support for training more than 2^32 observations may be enabled by recompiling.
• New option impPermute introduces permutation-based variable importance.

• Following the introduction of standalone sampling, a break in backward compatibility appears in versions 0.3-0 and higher of the Rborist package. Prediction with models trained using earlier versions throws an unidentified-index exception from within the Rcpp glue layer. Older models should therefore be retrained in order to use version 0.3-0 and above.
• Documentation for the validate command are out of date and may lead to errors. The documentation has been revised with version 0.3-8.
• Classification was reporting the oobErr value as accuracy instead of out-of-bag error. This has been repaired in 0.3-8.
• Non-binary classification with moderately-sized factor predictors (>= 64 levels) was failing due a wraparound shift. Repaired in 0.3-8.

Correctness and runtime errors are addressed as received. With reproducible test cases, repairs are typically uploaded to the Rborst.CRAN repository within several days.

Feature requests are addressed on a case-by-case basis.