Class "RandomForest"
A class for representing random forest ensembles.
Objects can be created by calls of the form new("RandomForest", ...).
ensemble:Object of class "list", each element
being an object of class "BinaryTree".
data: an object of class "ModelEnv".
initweights:a vector of initial weights.
weights:a list of weights defining the sub-samples.
where:a matrix of integers vectors of length n (number of observations in the learning sample) giving the number of the terminal node the corresponding observations is element of (in each tree).
data: an object of class "ModelEnv".
responses: an object of class "VariableFrame"
storing the values of the response variable(s).
cond_distr_response:a function computing the conditional distribution of the response.
predict_response:a function for computing predictions.
prediction_weights:a function for extracting weights from terminal nodes.
get_where:a function for determining the number of terminal nodes observations fall into.
update:a function for updating weights.
signature(object = "RandomForest"): ...
signature(object = "RandomForest"): ...
signature(object = "RandomForest"): ...
set.seed(290875)
### honest (i.e., out-of-bag) cross-classification of
### true vs. predicted classes
data("mammoexp", package = "TH.data")
table(mammoexp$ME, predict(cforest(ME ~ ., data = mammoexp,
control = cforest_unbiased(ntree = 50)),
OOB = TRUE))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.