.BT_relative_influence {BT} | R Documentation |
Method for estimating the relative influence.
Description
Helper function for computing the relative influence of each variable in the BT object.
Usage
.BT_relative_influence(
BTFit_object,
n.iter,
rescale = FALSE,
sort.it = FALSE,
consider.competing = FALSE,
consider.surrogates = FALSE
)
Arguments
BTFit_object |
a |
n.iter |
number of boosting iterations used for computation. If not provided, the function will perform a best guess approach to determine the optimal number of iterations. In fact, if a validation set was used during the fitting, the retained number of iterations is the one corresponding to the lowest validation set error ; otherwise, if cross-validation was performed, the number of iterations resulting in lowest cross-validation error will be used; otherwise, if the out-of-bag parameter was defined, the OOB error will be used to determine the optimal number of iterations; otherwise, all iterations will be used. |
rescale |
whether or not the results should be rescaled (divided by the maximum observation). Default set to |
sort.it |
whether or not the results should be (reverse) sorted. Default set to |
consider.competing |
whether or not competing split should be considered in the relative influence computation. Default set to |
consider.surrogates |
whether or not surrogates should be considered in the relative influence computation. Default set to |
Details
This function is not intended for end-user use. It performs the relative influence computation and is called during the summary function. Note that a permutation approach is not yet implemented.
Value
Returns by default an unprocessed vector of estimated relative influences. If the rescale
and sort.it
arguments are used, it returns
a processed version of the same vector.
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.