Returns a vector of predicted responses from a fitted itree
object.
This function is used and functions identically to predict.rpart
even for procedures
unique to itree
. However itree
does not currently support the type="matrix"
option.
1 2 3 4 
object 
fitted model object of class 
newdata 
data frame containing the values at which predictions are required.
The predictors referred to in the right side of

type 
character string denoting the type of predicted value returned. If
the 
na.action 
a function to determine what should be done with
missing values in 
... 
further arguments passed to or from other methods. 
This function is a method for the generic function predict for class
itree
. It can be invoked by calling predict
for an object
of the appropriate class, or directly by calling predict.itree
regardless of the class of the object.
A new object is obtained by
dropping newdata
down the object. For factor predictors, if an
observation contains a level not used to grow the tree, it is left at
the deepest possible node and frame$yval
at the node is the
prediction.
If type="vector"
:
vector of predicted responses.
For regression trees this is the mean response at the node, for Poisson
trees it is the estimated response rate, and for classification trees
it is the predicted class (as a number).
If type="prob"
:
(for a classification tree) a matrix of class probabilities.
If type="matrix"
:
For regression trees, this is the mean response, and for classification
trees it is the concatenation of the predicted class, the class counts at that node in the fitted tree, and
the class probabilities.
If type="class"
:
(for a classification tree) a factor of classifications based on the
responses.
predict
, itree.object
1 2 3 4 5 6 7 8 9 10 11 12 13 14  #verbatim from rpart examples:
z.auto < itree(Mileage ~ Weight, car.test.frame)
predict(z.auto)
fit < itree(Kyphosis ~ Age + Number + Start, data=kyphosis)
predict(fit, type="prob") # class probabilities (default)
predict(fit, type="vector") # level numbers
predict(fit, type="class") # factor
predict(fit, type="matrix") # level number, class frequencies, probabilities
sub < c(sample(1:50, 25), sample(51:100, 25), sample(101:150, 25))
fit < itree(Species ~ ., data=iris, subset=sub)
fit
table(predict(fit, iris[sub,], type="class"), iris[sub, "Species"])

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