R: TDboost Tweedie Regression Model Object

TDboost.object {TDboost} | R Documentation

## TDboost Tweedie Regression Model Object

### Description

These are objects representing fitted `TDboost`s.

### 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 yi.yang6@mcgill.ca, Wei Qian wxqsma@rit.edu and Hui Zou hzou@stat.umn.edu

### See Also

`TDboost`

[Package TDboost version 1.2 Index]