TDboost.object: TDboost Tweedie Regression Model Object

In emeryyi/tdboost: A Boosted Tweedie Compound Poisson Model

TDboost Tweedie Regression Model Object

Description

These are objects representing fitted TDboosts.

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
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 TDboost.perf
trees	a list containing the tree structures.
c.splits	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

Structure

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

Author(s)

Yi Yang [e-mail], Wei Qian [e-mail] and Hui Zou [e-mail]

See Also

TDboost

emeryyi/tdboost documentation built on Aug. 16, 2022, 7:17 a.m.