These are objects representing fitted TDboosts.
the "intercept" term, the initial predicted value to which trees make adjustments
a vector containing the fitted values on the scale of regression function
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
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
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
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 TDboost.perf
a list containing the tree structures.
a list of all the categorical splits in the collection of
trees. If the trees[[i]] component of a TDboost 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 TDboost object.
Yi Yang yi.yang6@mcgill.ca, Wei Qian wxqsma@rit.edu and Hui Zou hzou@stat.umn.edu
TDboost