summary.BTFit {BT} | R Documentation |
Computes the relative influence of each variable in the BTFit object.
## S3 method for class '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,
...
)
object |
a |
cBars |
the number of bars to plot. If |
n.iter |
the number of trees used to compute the relative influence. Only the first |
plot_it |
an indicator as to whether the plot is generated. |
order_it |
an indicator as to whether the plotted and/or returned relative influences are sorted. |
method |
the function used to compute the relative influence. Currently, only |
normalize |
if |
... |
additional argument passed to the plot function. |
Please note that the relative influence for variables having an original negative relative influence is forced to 0.
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.
Gireg Willame gireg.willame@gmail.com
This package is inspired by the gbm3
package. For more details, see https://github.com/gbm-developers/gbm3/.
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.