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.