erboost.object: 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,="" a="" vector="" of="" length="" equal="" to="" the="" number="" fitted="" trees="" containing="" cross-validated="" estimate="" loss="" function="" for="" each="" boosting="" iteration<="" dd=""> - 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.