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 i-th CV-fold, for the model having been trained on the data in all other folds.