predict.oblique.tree {oblique.tree} | R Documentation |
Returns predictions from a fitted oblique.tree
object.
predict.oblique.tree( object, newdata, type = c("vector", "tree", "class", "where"), eps = 1e-3, update.tree.predictions = FALSE, ...)
object |
Fitted model object of class oblique.tree . This is assumed to be the result of some function that produces an object with the same named components as that returned by oblique.tree . |
newdata |
Data frame containing the values at which predictions are required. The predictors referred to in the right side of formula(object) must be present by name in newdata . If missing, fitted values are returned. |
type |
Character string denoting how predictions are to be returned, i.e. class probabilities (default), a tree object, class predictions or predictions to leaf nodes. |
eps |
A lower bound for the probabilities, used if events of predicted probability zero occur in newdata when predicting a tree. |
update.tree.predictions |
Logical vector denoting whether tree predictions (frame$yval, frame$yprob, $where, $y etc) are updated when newdata is provided. |
... |
Further arguments passed to or from other methods. |
This function is a method for the generic function predict()
for objects of class c("oblique.tree","tree")
. It can be invoked by calling predict(x)
for an object x
of the appropriate class or directly by calling predict.oblique.tree(x)
regardless of the class of the object.
If type = "vector"
:
a matrix of predicted class probabilities is returned. This object is obtained by dropping observations down object
.
If type = "tree"
:
an object of class c("oblique.tree","tree")
is returned with new values for frame$n
and frame$dev
.
If type = "class"
:
a factor of the predicted classes (that with highest posterior probability, with ties split randomly).
If type = "where"
:
the nodes the cases reach.
A. Truong
Truong. A (2009) Fast Growing and Interpretable Oblique Trees via Probabilistic Models
Ripley, B. D. (1996). Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge. Chapter 7.
#grow an oblique tree to the Pima Indian dataset data(Pima.tr, package = "MASS") ob.tree <- oblique.tree(formula = type~., data = Pima.tr, oblique.splits = "on") plot(ob.tree);text(ob.tree);title(main="Oblique Tree") #predictions to in-sample data #class probabilities for each observation predict(ob.tree,type="vector") #the tree itself predict(ob.tree,type="tree") #class predictions for each observation predict(ob.tree,type="class") #the leaf where each observation falls predict(ob.tree,type="where") #predictions to out-of-sample data data(Pima.te, package = "MASS") #class probabilities for each observation predict(ob.tree,newdata=Pima.te,type="vector") #the tree itself predict(ob.tree,newdata=Pima.te,type="tree") #class predictions for each observation predict(ob.tree,newdata=Pima.te,type="class") #the leaf where each observation falls predict(ob.tree,newdata=Pima.te,type="where")