R: Summary of a BTFit object.

summary.BTFit {BT}

R Documentation

Summary of a BTFit object.

Description

Computes the relative influence of each variable in the BTFit object.

Usage

## 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,
  ...
)

Arguments

`object`	a `BTFit` object.
`cBars`	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.
`n.iter`	the number of trees used to compute the relative influence. Only the first `n.iter` trees will be used.
`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 `.BT_relative_influence` is available (default value as well).
`normalize`	if `TRUE` returns the normalized relative influence.
`...`	additional argument passed to the plot function.

Details

Please note that the relative influence for variables having an original negative relative influence is forced to 0.

Value

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.

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.