gbm_object: Generalized Boosted Regression Model Object

In gbm-developers/gbm3: Generalized Boosted Regression Models

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
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 gbmt_performance
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.

Structure

The following components must be included in a legitimate GBMFit object.

Author(s)

Greg Ridgeway [e-mail]

See Also

gbmt

gbm-developers/gbm3 documentation built on March 8, 2024, 4:48 p.m.