Description Usage Arguments Value Author(s) Examples
Accelerated implementation of the Random Forest (trademarked name) algorithm. Tuned for multicore and GPU hardware. Bindable with most numerical frontend languages in addtion to R. Invocation is similar to that provided by "randomForest" package.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  ## Default S3 method:
Rborist(x,
y,
autoCompress = 0.25,
ctgCensus = "votes",
classWeight = NULL,
maxLeaf = 0,
minInfo = 0.01,
minNode = ifelse(is.factor(y), 2, 3),
nLevel = 0,
nSamp = 0,
nThread = 0,
nTree = 500,
noValidate = FALSE,
predFixed = 0,
predProb = 0.0,
predWeight = NULL,
quantVec = NULL,
quantiles = !is.null(quantVec),
regMono = NULL,
rowWeight = NULL,
splitQuant = NULL,
thinLeaves = ifelse(is.factor(y), TRUE, FALSE),
treeBlock = 1,
verbose = FALSE,
withRepl = TRUE,
...)

x 
the design matrix expressed as a 
y 
the response (outcome) vector, either numerical or
categorical. Row count must conform with 
autoCompress 
plurality above which to compress predictor values. 
ctgCensus 
report categorical validation by vote or by probability. 
classWeight 
proportional weighting of classification categories. 
maxLeaf 
maximum number of leaves in a tree. Zero denotes no limit. 
minInfo 
information ratio with parent below which node does not split. 
minNode 
minimum number of distinct row references to split a node. 
nLevel 
maximum number of tree levels to train. Zero denotes no limit. 
nSamp 
number of rows to sample, per tree. 
nThread 
suggests an OpenMPstyle thread count. Zero denotes the default processor setting. 
nTree 
the number of trees to train. 
noValidate 
whether to train without validation. 
predFixed 
number of trial predictors for a split ( 
predProb 
probability of selecting individual predictor as trial splitter. 
predWeight 
relative weighting of individual predictors as trial splitters. 
quantVec 
quantile levels to validate. 
quantiles 
whether to report quantiles at validation. 
regMono 
signed probability constraint for monotonic regression. 
rowWeight 
row weighting for initial sampling of tree. 
splitQuant 
(sub)quantile at which to place cut point for numerical splits 
.
thinLeaves 
bypasses creation of export and quantile state in order to reduce memory footprint. 
treeBlock 
maximum number of trees to train during a single level (e.g., coprocessor computing). 
verbose 
indicates whether to output progress of training. 
withRepl 
whether row sampling is by replacement. 
... 
not currently used. 
an object of class Rborist
, a list containing the
following items:
forest 
a list containing

a list containing either of:
LeafReg
a list consisting of regression leaf data:
node
a packed structure expressing leaf scores and node counts.
nodeHeight
a vector of accumulated tree heights within
node
.
bagHeight
a vector of accumulated bag counts, per tree.
bagSample
a vector of packed data structures, one per
unique row sample, containing the row index and number of times sampled.
yTrain
the training response.
or
LeafCtg
a list consisting of classification leaf data:
node
a packed structure expressing leaf scores and node counts.
nodeHeight
a vector of accumulated tree heights within
node
.
bagHeight
a vector of accumulated bag counts, per tree.
bagSample
a vector of packed data structures, one per
unique row sample, containing the row index and number of times sampled.
weight
a vector of percategory probabilities, one set for
each sampled row.
levels
a vector of strings containing the training response levels.
bag 
a list consisting of bagged row information:

training 
a list containing information gleaned during training:

validation 
a list containing the results of validation, if requested:

Mark Seligman at Suiji.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102  ## Not run:
# Regression example:
nRow < 5000
x < data.frame(replicate(6, rnorm(nRow)))
y < with(x, X1^2 + sin(X2) + X3 * X4) # courtesy of S. Welling.
# Classification example:
data(iris)
# Generic invocation:
rb < Rborist(x, y)
# Causes 300 trees to be trained:
rb < Rborist(x, y, nTree = 300)
# Causes rows to be sampled without replacement:
rb < Rborist(x, y, withRepl=FALSE)
# Causes validation census to report class probabilities:
rb < Rborist(iris[5], iris[5], ctgCensus="prob")
# Applies tableweighting to classification categories:
rb < Rborist(iris[5], iris[5], classWeight = "balance")
# Weights first category twice as heavily as remaining two:
rb < Rborist(iris[5], iris[5], classWeight = c(2.0, 1.0, 1.0))
# Does not split nodes when doing so yields less than a 2% gain in
# information over the parent node:
rb < Rborist(x, y, minInfo=0.02)
# Does not split nodes representing fewer than 10 unique samples:
rb < Rborist(x, y, minNode=10)
# Trains a maximum of 20 levels:
rb < Rborist(x, y, nLevel = 20)
# Trains, but does not perform subsequent validation:
rb < Rborist(x, y, noValidate=TRUE)
# Chooses 500 rows (with replacement) to root each tree.
rb < Rborist(x, y, nSamp=500)
# Chooses 2 predictors as splitting candidates at each node (or
# fewer, when choices exhausted):
rb < Rborist(x, y, predFixed = 2)
# Causes each predictor to be selected as a splitting candidate with
# distribution Bernoulli(0.3):
rb < Rborist(x, y, predProb = 0.3)
# Causes first three predictors to be selected as splitting candidates
# twice as often as the other two:
rb < Rborist(x, y, predWeight=c(2.0, 2.0, 2.0, 1.0, 1.0))
# Causes (default) quantiles to be computed at validation:
rb < Rborist(x, y, quantiles=TRUE)
qPred < rb$validation$qPred
# Causes specfied quantiles (deciles) to be computed at validation:
rb < Rborist(x, y, quantVec = seq(0.1, 1.0, by = 0.10))
qPred < rb$validation$qPred
# Constrains modelled response to be increasing with respect to X1
# and decreasing with respect to X5.
rb < Rborist(x, y, regMono=c(1.0, 0, 0, 0, 1.0, 0))
# Causes rows to be sampled with random weighting:
rb < Rborist(x, y, rowWeight=runif(nRow))
# Suppresses creation of detailed leaf information needed for
# quantile prediction and external tools.
rb < Rborist(x, y, thinLeaves = TRUE)
# Sets splitting position for predictor 0 to far left and predictor
# 1 to far right, others to default (median) position.
spq < rep(0.5, ncol(x))
spq[0] < 0.0
spq[1] < 1.0
rb < Rborist(x, y, splitQuant = spq)
## End(Not run)

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