Fit Random Forest

method in classRandomForestSemisupervised used to build a Decision Tree

# S4 method for 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