BT_more {BT} | R Documentation |
Perform additional boosting iterations.
Description
Method to perform additional iterations of the Boosting Tree algorithm, starting from an initial BTFit
object.
This does not support further cross-validation. Moreover, this approach is only allowed if keep.data=TRUE
in the original call.
Usage
BT_more(BTFit_object, new.n.iter = 100, is.verbose = FALSE, seed = NULL)
Arguments
BTFit_object |
a |
new.n.iter |
number of new boosting iterations to perform. |
is.verbose |
a logical specifying whether or not the additional fitting should run "noisely" with feedback on progress provided to the user. |
seed |
optional seed used to perform the new iterations. By default, no seed is set. |
Value
Returns a new BTFit
object containing the initial call as well as the new iterations performed.
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.