View source: R/relative-influence.r
relative_influence | R Documentation |
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)
gbm_fit_obj |
a |
num_trees |
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. |
rescale |
whether or not the result should be scaled. Defaults
to |
sort_it |
whether or not the results should be (reverse)
sorted. Defaults to |
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).
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.
James Hickey, Greg Ridgeway gregridgeway@gmail.com
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
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