Predicted values based on a generalized boosted model object
# S3 method for gbm
predict(object,
newdata,
n.trees,
type="link",
single.tree=FALSE,
...)
Object of class inheriting from (gbm.object
)
Data frame of observations for which to make predictions
Number of trees used in the prediction. n.trees
may
be a vector in which case predictions are returned for each
iteration specified
The scale on which gbm makes the predictions
If single.tree=TRUE
then predict.gbm
returns
only the predictions from tree(s) n.trees
further arguments passed to or from other methods
Returns a vector of predictions. By default the predictions are on the scale of f(x). For example, for the Bernoulli loss the returned value is on the log odds scale, poisson loss on the log scale, and coxph is on the log hazard scale.
If type="response"
then gbm
converts back to the same scale as the outcome. Currently the only effect this will have is returning probabilities for bernoulli and expected counts for poisson. For the other distributions "response" and "link" return the same.
predict.gbm
produces predicted values for each observation in newdata
using the the first n.trees
iterations of the boosting sequence. If n.trees
is a vector than the result is a matrix with each column representing the predictions from gbm models with n.trees[1]
iterations, n.trees[2]
iterations, and so on.
The predictions from gbm
do not include the offset term. The user may add the value of the offset to the predicted value if desired.
If object
was fit using gbm.fit
there will be no
Terms
component. Therefore, the user has greater responsibility to make
sure that newdata
is of the same format (order and number of variables)
as the one originally used to fit the model.