predict method for random forest objects
Prediction of test data using random forest.
## S3 method for class 'randomForest' predict(object, newdata, type="response", norm.votes=TRUE, predict.all=FALSE, proximity=FALSE, nodes=FALSE, cutoff, ...)
object |
an object of class |
newdata |
a data frame or matrix containing new data. (Note: If
not given, the out-of-bag prediction in |
type |
one of |
norm.votes |
Should the vote counts be normalized (i.e.,
expressed as fractions)? Ignored if |
predict.all |
Should the predictions of all trees be kept? |
proximity |
Should proximity measures be computed? An error is
issued if |
nodes |
Should the terminal node indicators (an n by ntree matrix) be return? If so, it is in the “nodes” attribute of the returned object. |
cutoff |
(Classification only) A vector of length equal to
number of classes. The ‘winning’ class for an observation is the
one with the maximum ratio of proportion of votes to cutoff.
Default is taken from the |
... |
not used currently. |
If object$type is regression, a vector of predicted
values is returned. If predict.all=TRUE, then the returned
object is a list of two components: aggregate, which is the
vector of predicted values by the forest, and individual, which
is a matrix where each column contains prediction by a tree in the
forest.
If object$type is classification, the object returned
depends on the argument type:
response |
predicted classes (the classes with majority vote). |
prob |
matrix of class probabilities (one column for each class and one row for each input). |
vote |
matrix of vote counts (one column for each class
and one row for each new input); either in raw counts or in fractions
(if |
If 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.
NOTE: If the object inherits from randomForest.formula,
then any data with NA are silently omitted from the prediction.
The returned value will contain NA correspondingly in the
aggregated and individual tree predictions (if requested), but not in
the proximity or node matrices.
NOTE2: Any ties are broken at random, so if this is undesirable, avoid it by
using odd number ntree in randomForest().
Andy Liaw andy\_liaw@merck.com and Matthew Wiener matthew\_wiener@merck.com, based on original Fortran code by Leo Breiman and Adele Cutler.
Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.
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) ## Get prediction for all trees. predict(iris.rf, iris[ind == 2,], predict.all=TRUE) ## Proximities. predict(iris.rf, iris[ind == 2,], proximity=TRUE) ## Nodes matrix. str(attr(predict(iris.rf, iris[ind == 2,], nodes=TRUE), "nodes"))
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