Computes the relative influence of each variable in the BTFit object.
# S3 method for BTFit
summary(
object,
cBars = length(object$var.names),
n.iter = object$BTParams$n.iter,
plot_it = TRUE,
order_it = TRUE,
method = .BT_relative_influence,
normalize = TRUE,
...
)Returns a data frame where the first component is the variable name and the second one is the computed relative influence, normalized to sum up to 100.
Depending on the plot_it value, the relative influence plot will be performed.
a BTFit object.
the number of bars to plot. If order=TRUE only the variables with the cBars largest relative influence will appear in the barplot.
If order=FALSE then the first cBars variables will appear in the barplot.
the number of trees used to compute the relative influence. Only the first n.iter trees will be used.
an indicator as to whether the plot is generated.
an indicator as to whether the plotted and/or returned relative influences are sorted.
the function used to compute the relative influence. Currently, only .BT_relative_influence is available (default value as well).
if TRUE returns the normalized relative influence.
additional argument passed to the plot function.
Gireg Willame gireg.willame@gmail.com
This package is inspired by the gbm3 package. For more details, see https://github.com/gbm-developers/gbm3/.
Please note that the relative influence for variables having an original negative relative influence is forced to 0.
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.
BT, .BT_relative_influence.