gbm (version 0.6)

gbm.object: Generalized Boosted Regression Model Object

Description

These are objects representing fitted gbms.

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

gbm