R: TDboost performance

TDboost.perf {TDboost}

R Documentation

TDboost performance

Estimates the optimal number of boosting iterations for a TDboost object and optionally plots various performance measures

TDboost.perf(object, 
         plot.it = TRUE, 
         oobag.curve = FALSE, 
         overlay = TRUE, 
         method)

`object`	a `TDboost.object` created from an initial call to `TDboost`.
`plot.it`	an indicator of whether or not to plot the performance measures. Setting `plot.it=TRUE` creates two plots. The first plot plots `object$train.error` (in black) and `object$valid.error` (in red) versus the iteration number. The scale of the error measurement, shown on the left vertical axis, depends on the `distribution` argument used in the initial call to `TDboost`.
`oobag.curve`	indicates whether to plot the out-of-bag performance measures in a second plot.
`overlay`	if TRUE and oobag.curve=TRUE then a right y-axis is added to the training and test error plot and the estimated cumulative improvement in the loss function is plotted versus the iteration number.
`method`	indicate the method used to estimate the optimal number of boosting iterations. `method="OOB"` computes the out-of-bag estimate and `method="test"` uses the test (or validation) dataset to compute an out-of-sample estimate. `method="cv"` extracts the optimal number of iterations using cross-validation if `TDboost` was called with `cv.folds`>1

TDboost.perf returns the estimated optimal number of iterations. The method of computation depends on the method argument.

Yang, Y., Qian, W. and Zou, H. (2013), “A Boosted Tweedie Compound Poisson Model for Insurance Premium” Preprint.

G. Ridgeway (1999). “The state of boosting,” Computing Science and Statistics 31:172-181.

G. Ridgeway (2003). "A note on out-of-bag estimation for estimating the optimal number of boosting iterations," Working paper.