Methods for estimating relative influence

This function offers a method for computing the relative influence in summary.GBMFit, and is not intended to be called directly.

Usage

relative_influence(gbm_fit_obj, num_trees, rescale = FALSE, sort_it = FALSE)

Arguments

gbm_fit_obj: a GBMFit object created from an initial call to gbmt.
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 FALSE.
sort_it: whether or not the results should be (reverse) sorted. Defaults to FALSE.

Value

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.

Details

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).

References

J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(5):1189-1232.

L. Breiman (2001). Random Forests.

Author

James Hickey, Greg Ridgeway gregridgeway@gmail.com