relative.influence function

Methods for estimating relative influence

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)

Arguments

• object: a gbm object created from an initial call to gbm.
• n.trees: 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.
• scale.: whether or not the result should be scaled. Defaults to FALSE.
• sort.: whether or not the results should be (reverse) sorted. Defaults to FALSE.
• y, f, w, offset, dist, baseline: For gbm.loss: These components are the outcome, predicted value, observation weight, offset, distribution, and comparison loss function, respectively.
• group, max.rank: Used internally when distribution = \'pairwise\'.

Returns

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.

Details

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.

References

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.

See Also

summary.gbm

Author(s)

Greg Ridgeway gregridgeway@gmail.com