## S3 method for class 'randomForest':
predict(object, newdata, type="response",
norm.votes=TRUE, predict.all=FALSE, proximity=FALSE, nodes=FALSE,
cutoff, ...)
randomForest
, as that
created by the function randomForest
.object
is returned.response
, prob
. or votes
,
indicating the type of output: predicted values, matrix of class
probabilities, or matrix of vote counts. class
is allowed, but
automatically converted to "responsobject$type
is
regression
.object$type
is regression
.forest$cutoff
component ofobject$type
is regression
, a vector of predicted
values is returned. If predict.all=TRUE
, then the returned
object is a list of two components: aggregate
, which is the
vector of predicted values by the forest, and individual
, which
is a matrix where each column contains prediction by a tree in the
forest. If object$type
is classification
, the object returned
depends on the argument type
:
norm.votes=TRUE
).predict.all=TRUE
, then the individual
component of the
returned object is a character matrix where each column contains the
predicted class by a tree in the forest.If proximity=TRUE
, the returned object is a list with two
components: pred
is the prediction (as described above) and
proximity
is the proximitry matrix. An error is issued if
object$type
is regression
.
If nodes=TRUE
, the returned object has a ``nodes'' attribute,
which is an n by ntree matrix, each column containing the node number
that the cases fall in for that tree.
randomForest
data(iris)
set.seed(111)
ind <- sample(2, nrow(iris), replace = TRUE, prob=c(0.8, 0.2))
iris.rf <- randomForest(Species ~ ., data=iris[ind == 1,])
iris.pred <- predict(iris.rf, iris[ind == 2,])
table(observed = iris[ind==2, "Species"], predicted = iris.pred)
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