BT_more {BT}

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 BTFit object.

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 [e-mail]

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, BTFit.