BT_perf: Performance assessment.

In BT: (Adaptive) Boosting Trees Algorithm

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 [e-mail]

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.

See Also

BT, BT_call.

BT documentation built on Aug. 19, 2023, 5:09 p.m.