I am trying to implement the AdaBoost.M1 algorithm (trees as base-learners) to a data set with a large feature space (~ 20.000 features) and ~ 100 samples in
R. There exists a variety of different packages for this purpose;
gbm() (from the
gbm-package) appears to be my only available option, as
stack.overflow is a problem in the others, and though it works, it is very time-consuming.
- Is there any way to overcome the
stack.overflowthe problem in the other packages, or have the
gbm()run faster? I have tried converting the
data.frameinto a matrix without success.
- When performing
gbm()(with distribution set to "adaboost"), An Introduction to Statistical Learning (Hastie et al.) mentions the following parameters needed for tuning:
- The total number of trees to fit.
- The shrinkage parameter denoted lambda.
- The number of splits in each tree, controlling the complexity of the boosted ensemble.
As the algorithm is very time consuming to perform in
R, I need to find literature on what tuning parameters that are within a suitable range for this kind of large feature space data, before performing cross-validation over the range to estimate the test error rate.