# gbm.object

From gbm v0.6
by Greg Ridgeway

##### Generalized Boosted Regression Model Object

These are objects representing fitted `gbm`

s.

- Keywords
- methods

##### Value

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 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.

##### Structure

The following components must be included in a legitimate `gbm`

object.

##### See Also

*Documentation reproduced from package gbm, version 0.6, License: GPL (version 2 or newer)*

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