BT_perf {BT}  R Documentation 
Function to compute the performances of a fitted boosting tree.
BT_perf(
BTFit_object,
plot.it = TRUE,
oobag.curve = FALSE,
overlay = TRUE,
method,
main = ""
)
BTFit_object 

plot.it 
a boolean indicating whether to plot the performance measure. Setting 
oobag.curve 
indicates whether to plot the outofbag performance measures in a second plot. Note that this option makes sense if the 
overlay 
if set to 
method 
indicates the method used to estimate the optimal number of boosting iterations. Setting 
main 
optional parameter that allows the user to define specific plot title. 
Returns the estimated optimal number of iterations. The method of computation depends on the method
argument.
Gireg Willame g.willame@detralytics.eu
This package is inspired by the gbm3
package. For more details, see https://github.com/gbmdevelopers/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 : TreeBased 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 loglink. Accepted for publication in Scandinavian Actuarial Journal.
M. Denuit, J. Huyghe and J. Trufin (2022). Boosting costcomplexity 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.