relative.influence: Methods for estimating relative influence

In gbm: Generalized Boosted Regression Models

Methods for estimating relative influence

Description

Helper functions for computing the relative influence of each variable in the gbm object.

Usage

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\'.

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.

Value

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.

Author(s)

Greg Ridgeway [e-mail]

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

gbm documentation built on June 28, 2024, 9:07 a.m.