predict.randomForest {randomForest} | R Documentation |
Prediction of test data using random forest.
predict(object, newdata, type="response", norm.votes=TRUE, proximity=FALSE, ...)
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)? Ignored if object$type is
regression . |
proximity |
Should proximity measures be computed? An error is
issued if object$type is regression . |
... |
not used currently. |
If object$type
is regression
, a vector of predicted
values is returned.
If object$type
is classification
, the vector 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). |
votes |
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 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
.
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)