gbm.object {gbm} | R Documentation |
These are objects representing fitted gbm
s.
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 |
The following components must be included in a legitimate gbm
object.
Greg Ridgeway gregr@rand.org