This function offers a method for computing the relative influence in
summary.GBMFit, and is not intended to be called directly.
relative_influence(gbm_fit_obj, num_trees, rescale = FALSE, sort_it = FALSE)By default, returns an unprocessed vector of estimated
relative influences. If the rescale and sort
arguments are used, returns a processed version of the same.
a GBMFit object created from an initial
call to gbmt.
the number of trees to use for computations. If not provided, 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.
James Hickey, Greg Ridgeway gregridgeway@gmail.com
relative_influence is the same as that
described in Friedman (2001).
permutation_relative_influence randomly permutes each
predictor variable at a time and computes the associated reduction in
predictive performance. This is similar to the variable importance measures
Breiman uses for random forests, but gbmt currently computes using the
entire training dataset (not the out-of-bag observations).
J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(5):1189-1232.
L. Breiman (2001). Random Forests.
summary.GBMFit