These are objects representing fitted gbms.
the "intercept" term, the initial predicted value to which trees make adjustments
a vector containing the fitted values on the scale of regression function (e.g. log-odds scale for bernoulli, log scale for poisson)
a vector of length equal to the number of fitted trees containing the value of the loss function for each boosting iteration evaluated on the training data
a vector of length equal to the number of fitted trees containing the value of the loss function for each boosting iteration evaluated on the validation data
if cv.folds<2 this
component is NULL. Otherwise, this component is a vector of length equal to
the number of fitted trees containing a cross-validated estimate of the loss
function for each boosting iteration
a vector of
length equal to the number of fitted trees containing an out-of-bag estimate
of the marginal reduction in the expected value of the loss function. The
out-of-bag estimate uses only the training data and is useful for estimating
the optimal number of boosting iterations. See gbm.perf
a list containing the tree structures. The components are best
viewed using pretty.gbm.tree
a list of all
the categorical splits in the collection of trees. If the trees[[i]]
component of a gbm object describes a categorical split then the
splitting value will refer to a component of c.splits. That component
of c.splits will be a vector of length equal to the number of levels
in the categorical split variable. -1 indicates left, +1 indicates right,
and 0 indicates that the level was not present in the training data
If cross-validation was performed, the cross-validation predicted values on the scale of the linear predictor. That is, the fitted values from the ith CV-fold, for the model having been trained on the data in all other folds.
The following components must be included in a
legitimate gbm object.