fit_random_forest,RandomForestSemisupervised-method {SSLR} | R Documentation |
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 |
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
[Package SSLR version 0.9.3.3 Index]