erboost.object: ER-Boost Expectile Regression Model Object

In erboost: Nonparametric Multiple Expectile Regression via ER-Boost

ER-Boost Expectile Regression Model Object

Description

These are objects representing fitted erboosts.

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 erboost.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 erboost 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 erboost object.

Author(s)

Yi Yang [e-mail] and Hui Zou [e-mail]

See Also

erboost

erboost documentation built on April 3, 2025, 7:52 p.m.