R: Performance assessment.

BT_perf {BT}

R Documentation

Performance assessment.

Description

Function to compute the performances of a fitted boosting tree.

Usage

BT_perf(
  BTFit_object,
  plot.it = TRUE,
  oobag.curve = FALSE,
  overlay = TRUE,
  method,
  main = ""
)

Arguments

`BTFit_object`	a `BTFit` object resulting from an initial call to `BT`
`plot.it`	a boolean indicating whether to plot the performance measure. Setting `plot.it = TRUE` creates two plots. The first one plots the `object$BTErrors$training.error` (in black) as well as the `object$BTErrors$validation.error` (in red) and/or the `object$BTErrors$cv.error` (in green) depending on the `method` and parametrization. These values are plotted as a function of the iteration number. The scale of the error measurement, shown on the left vertical axis, depends on the arguments used in the initial call to `BT` and the chosen `method`.
`oobag.curve`	indicates whether to plot the out-of-bag performance measures in a second plot. Note that this option makes sense if the `bag.fraction` was properly defined in the initial call to `BT`.
`overlay`	if set to `TRUE` and `oobag.curve=TRUE` then a right y-axis is added and the estimated cumulative improvement in the loss function is plotted versus the iteration number.
`method`	indicates the method used to estimate the optimal number of boosting iterations. Setting `method = "OOB"` computes the out-of-bag estimate and `method = "validation"` uses the validation dataset to compute an out-of-sample estimate. Finally, setting `method = "cv"` extracts the optimal number of iterations using cross-validation, if `BT` was called with `cv.folds > 1`. If missing, a guessing method is applied.
`main`	optional parameter that allows the user to define specific plot title.

Value

Returns the estimated optimal number of iterations. The method of computation depends on the method argument.

Author(s)

Gireg Willame g.willame@detralytics.eu

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.