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SSLR

fit_random_forest-RandomForestSemisupervised-method

Fit Random Forest

Description

method in classRandomForestSemisupervised used to build a Decision Tree

Usage

## S4 method for signature 'RandomForestSemisupervised'
fit_random_forest(
  object,
  X,
  y,
  mtry = 2,
  trees = 500,
  min_n = 2,
  w = 0.5,
  replace = TRUE,
  tree_max_depth = Inf,
  sampsize = if (replace) nrow(X) else ceiling(0.632 * nrow(X)),
  min_samples_leaf = if (!is.null(y) && !is.factor(y)) 5 else 1,
  allowParallel = TRUE
)

Arguments

object

A RandomForestSemisupervised object
X

A object that can be coerced as data.frame. Training instances
y

A vector with the labels of the training instances. In this vector the unlabeled instances are specified with the value NA.
mtry

number of features in each decision tree
trees

number of trees. Default is 5
min_n

number of minimum samples in each tree
w

weight parameter ranging from 0 to 1
replace

replacing type in sampling
tree_max_depth

maximum tree depth. Default is Inf
sampsize

Size of sample. Default if (replace) nrow(x) else ceiling(.632*nrow(x))
min_samples_leaf

the minimum number of any terminal leaf node
allowParallel

Execute Random Forest in parallel if doParallel is loaded. Default is TRUE

Value

list of decision trees

SSLR

Semi-Supervised Classification, Regression and Clustering Methods

v0.9.3.1
GPL-3
Authors
Francisco Jesús Palomares Alabarce [aut, cre] (<https://orcid.org/0000-0002-0499-7034>), José Manuel Benítez [ctb] (<https://orcid.org/0000-0002-2346-0793>), Isaac Triguero [ctb] (<https://orcid.org/0000-0002-0150-0651>), Christoph Bergmeir [ctb] (<https://orcid.org/0000-0002-3665-9021>), Mabel González [ctb] (<https://orcid.org/0000-0003-0152-444X>)
Initial release

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