Helper functions for computing the relative influence of each variable in the gbm object.
relative.influence(object, n.trees, scale. = FALSE, sort. = FALSE)permutation.test.gbm(object, n.trees)
gbm.loss(y, f, w, offset, dist, baseline, group = NULL, max.rank = NULL)
By default, returns an unprocessed vector of estimated relative
influences. If the scale. and sort. arguments are used,
returns a processed version of the same.
a gbm object created from an initial call to
gbm.
the number of trees to use for computations. If not provided, the the function will guess: if a test set was used in fitting, the number of trees resulting in lowest test set error will be used; otherwise, if cross-validation was performed, the number of trees resulting in lowest cross-validation error will be used; otherwise, all trees will be used.
whether or not the result should be scaled. Defaults to
FALSE.
whether or not the results should be (reverse) sorted.
Defaults to FALSE.
For gbm.loss: These components are
the outcome, predicted value, observation weight, offset, distribution, and
comparison loss function, respectively.
Used internally when distribution =
\'pairwise\'.
Greg Ridgeway gregridgeway@gmail.com
This is not intended for end-user use. These functions offer the different
methods for computing the relative influence in summary.gbm.
gbm.loss is a helper function for permutation.test.gbm.
J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(5):1189-1232.
L. Breiman (2001). https://www.stat.berkeley.edu/users/breiman/randomforest2001.pdf.
summary.gbm