gbm.object: Generalized Boosted Regression Model Object
In DexGroves/gbm-lrd: Generalized Boosted Regression Models

Description Value Structure Author(s) See Also

These are objects representing fitted gbms.

`initF`	the "intercept" term, the initial predicted value to which trees make adjustments
`fit`	a vector containing the fitted values on the scale of regression function (e.g. log-odds scale for bernoulli, log scale for poisson)
`train.error`	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
`valid.error`	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
`cv.error`	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
`oobag.improve`	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`
`trees`	a list containing the tree structures. The components are best viewed using `pretty.gbm.tree`
`c.splits`	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
`cv.fitted`	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.