predict.randomForest {randomForest}R Documentation

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, ...)


object an object of class randomForest, as that created by the function randomForest.
newdata a data frame or matrix containing new data. (Note: If not given, the out-of-bag prediction in object is returned.
type one of 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 "response", for backward compatibility.
norm.votes Should the vote counts be normalized (i.e., expressed as fractions)? Ignored if object$type is regression.
predict.all Should the predictions of all trees be kept?
proximity Should proximity measures be computed? An error is issued if object$type is regression.
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.
... 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 norm.votes=TRUE).

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.


Andy Liaw and Matthew Wiener, based on original Fortran code by Leo Breiman and Adele Cutler.


Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.

See Also



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)

[Package randomForest version 4.5-1 Index]