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randomForest — by Andy Liaw, a year ago

Breiman and Cutler's Random Forests for Classification and Regression

Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) .

ranger — by Marvin N. Wright, 4 months ago

A Fast Implementation of Random Forests

A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') can be directly analyzed.

missForest — by Daniel J. Stekhoven, 6 years ago

Nonparametric Missing Value Imputation using Random Forest

The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time.

randomForestSRC — by Udaya B. Kogalur, 10 days ago

Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)

Fast OpenMP parallel computing of Breiman's random forests for survival, competing risks, regression and classification based on Ishwaran and Kogalur's popular random survival forests (RSF) package. Handles missing data and now includes multivariate, unsupervised forests, quantile regression and solutions for class imbalanced data. New fast interface using subsampling and confidence regions for variable importance.

RRF — by Houtao Deng, 16 days ago

Regularized Random Forest

Feature Selection with Regularized Random Forest. This package is based on the 'randomForest' package by Andy Liaw. The key difference is the RRF() function that builds a regularized random forest.

grf — by Julie Tibshirani, 2 months ago

Generalized Random Forests (Beta)

A pluggable package for forest-based statistical estimation and inference. GRF currently provides methods for non-parametric least-squares regression, quantile regression, and treatment effect estimation (optionally using instrumental variables). This package is currently in beta, and we expect to make continual improvements to its performance and usability.

rerf — by Jesse Patsolic, 4 months ago

Randomer Forest

R-RerF (aka Randomer Forest (RerF) or Random Projection Forests) is an algorithm developed by Tomita (2016) which is similar to Random Forest - Random Combination (Forest-RC) developed by Breiman (2001) . Random Forests create axis-parallel, or orthogonal trees. That is, the feature space is recursively split along directions parallel to the axes of the feature space. Thus, in cases in which the classes seem inseparable along any single dimension, Random Forests may be suboptimal. To address this, Breiman also proposed and characterized Forest-RC, which uses linear combinations of coordinates rather than individual coordinates, to split along. This package, 'rerf', implements RerF which is similar to Forest-RC. The difference between the two algorithms is where the random linear combinations occur: Forest-RC combines features at the per tree level whereas RerF takes linear combinations of coordinates at every node in the tree.

ggRandomForests — by John Ehrlinger, 3 years ago

Visually Exploring Random Forests

Graphic elements for exploring Random Forests using the 'randomForest' or 'randomForestSRC' package for survival, regression and classification forests and 'ggplot2' package plotting.

iRF — by Karl Kumbier, 2 years ago

iterative Random Forests

Iteratively grows feature weighted random forests and finds high-order feature interactions in a stable fashion.

pRF — by Ankur Chakravarthy, 4 years ago

Permutation Significance for Random Forests

Estimate False Discovery Rates (FDRs) for importance metrics from random forest runs.