Estimates the optimal number of boosting iterations for a erboost object and
optionally plots various performance measures
erboost.perf(object,
plot.it = TRUE,
oobag.curve = FALSE,
overlay = TRUE,
method)erboost.perf returns the estimated optimal number of iterations. The method
of computation depends on the method argument.
a erboost.object created from an initial call to
erboost.
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 erboost.
indicates whether to plot the out-of-bag performance measures in a second plot.
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.
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 erboost was called
with cv.folds>1
Yi Yang yiyang@umn.edu and Hui Zou hzou@stat.umn.edu
Yang, Y. and Zou, H. (2015), “Nonparametric Multiple Expectile Regression via ER-Boost,” Journal of Statistical Computation and Simulation, 84(1), 84-95.
G. Ridgeway (1999). “The state of boosting,” Computing Science and Statistics 31:172-181.
erboost, erboost.object