predict.randomForest {RFO}R Documentation

predict method for random forest objects

Description

Prediction of test data using random forest.

Usage

## S3 method for class 'randomForest'
predict(object, newdata, type = "response",
  norm.votes = TRUE, cutoff, ...)

Arguments

object

an object of class randomForest, as that created by the function randomForest.

newdata

a data frame or matrix containing new data.

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)?

cutoff

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 cutoff component of object (i.e., the setting used when running randomForest).

...

not used currently.

Value

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).

The function currently does not support object$type regression.

NOTE: Any data with NA is silently omitted from the prediction. The returned value will contain NA correspondingly in the prediction.

NOTE2: Any ties are broken at random.

Author(s)

Lei Zhang lei.c.zhang@oracle.com, Andy Liaw andy\_liaw@merck.com and Matthew Wiener matthew\_wiener@merck.com, based on original Fortran code by Leo Breiman and Adele Cutler.

References

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

See Also

randomForest

Examples

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)

[Package RFO version 4.6-10 Index]