BTCVFit {BT} | R Documentation |
BTCVFit
Description
These are objects representing CV fitted boosting trees.
Details
CV (Adaptive) Boosting Tree Model Object.
Value
a list of BTFit
objects with each element corresponding to a specific BT fit on a particular fold
Structure
The following components must be included in a legitimate BTCVFit
object.
Author(s)
Gireg Willame gireg.willame@gmail.com
This package is inspired by the gbm3
package. For more details, see https://github.com/gbm-developers/gbm3/.
References
M. Denuit, D. Hainaut and J. Trufin (2019). Effective Statistical Learning Methods for Actuaries |: GLMs and Extensions, Springer Actuarial.
M. Denuit, D. Hainaut and J. Trufin (2019). Effective Statistical Learning Methods for Actuaries ||: Tree-Based Methods and Extensions, Springer Actuarial.
M. Denuit, D. Hainaut and J. Trufin (2019). Effective Statistical Learning Methods for Actuaries |||: Neural Networks and Extensions, Springer Actuarial.
M. Denuit, D. Hainaut and J. Trufin (2022). Response versus gradient boosting trees, GLMs and neural networks under Tweedie loss and log-link. Accepted for publication in Scandinavian Actuarial Journal.
M. Denuit, J. Huyghe and J. Trufin (2022). Boosting cost-complexity pruned trees on Tweedie responses: The ABT machine for insurance ratemaking. Paper submitted for publication.
M. Denuit, J. Trufin and T. Verdebout (2022). Boosting on the responses with Tweedie loss functions. Paper submitted for publication.
See Also
BT
.