Description Objects from the Class Slots Extends Methods
Class representing a classification random forest.
Objects can be created by calls of the form new("bigcforest", ...)
, but most often are generated by bigrfc
.
.Data
:Object of class "list"
. Each element is a "bigctree"
, representing a tree in the random classification forest.
nexamples
:Object of class "integer"
. Number of examples in the training set (including synthesized examples for unsupervised learning).
varselect
:Object of class "integer"
. Indices of the columns of x
that were used to train the model.
factorvars
:Object of class "logical"
. Indicates which variables are factors or categorical (TRUE
)), and which are numeric (FALSE
).
varnlevels
:Object of class "integer"
. Number of levels in each categorical variable, or 0
for numeric variables.
contvarseq
:Object of class "integer"
. Maps the continuous variables in varselect
to the columns in big.matrix
a
. Meant for internal use by bigrfc
or grow
when growing trees.
y
:Object of class "factor"
. Class labels for the training set.
ytable
:Object of class "table"
. Counts of training examples in each class.
yclasswts
:Object of class "matrix"
. One-dimensional matrix of scaled weights for each class.
ntrees
:Object of class "integer"
. Number of trees in the forest.
nsplitvar
:Object of class "integer"
. Number of variables to split on at each node.
maxndsize
:Object of class "integer"
. Maximum number of examples in each node when growing the trees.
maxeslevels
:Object of class "integer"
. Maximum number of levels for categorical variables for which exhaustive search of possible splits will be performed.
nrandsplit
:Object of class "integer"
Number of random splits to examine for categorical variables with more than maxeslevels
levels.
oobtimes
:Object of class "integer"
. Number of times each training example has been out-of-bag.
oobvotes
:Object of class "matrix"
. Out-of-bag votes for each training example.
oobpred
:Object of class "integer"
. Out-of-bag predictions for each training example.
trainclserr
:Object of class "numeric"
. Training errors of out-of-bag examples, by class.
trainerr
:Object of class "numeric"
. Total training error of out-of-bag examples.
trainconfusion
:Object of class "table"
. Confusion matrix for out-of-bag examples.
varginidec
:Object of class "numeric"
. Decrease in Gini impurity for each variable over all trees.
cachepath
:Object of class "character.or.NULL"
. Path to folder where data caches used in building the forest were stored, or NULL
if data was processed completely in memory.
Class "list"
, from data part.
Class "vector"
, by class "list", distance 2.
signature(forest = "bigcforest")
: Grow more trees in the random forest, using the same parameters. See grow
for details.
signature(x = "bigcforest", y = "bigcforest")
: Merge two random forests into one. See merge
for details.
signature(object = "bigcforest")
: Predict the classes of a set of test examples. See predict
for details.
signature(forest = "bigcforest")
: Compute variable importance based on out-of-bag estimates. See varimp
for details.
signature(forest = "bigcforest")
: Compute fast (Gini) variable importance. See fastimp
for details.
signature(forest = "bigcforest")
: Compute variable interactions. See interactions
for details.
signature(forest = "bigcforest")
: Compute the proximity matrix. See proximities
for details.
signature(forest = "bigcforest", prox = "bigrfprox")
: Compute class prototypes. See prototypes
for details.
signature(object = "bigcforest")
: Print the random forest.
signature(object = "bigcforest")
: Print summary information on the random forest, including out-of-bag training error estimates and the confusion matrix.
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