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Relative influence via permutation

Source: R/permutation-relative-influence.r
permutation_relative_influence.Rd

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

Usage

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

Arguments

gbm_fit_obj

a GBMFit object 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

Calculates the relative influence of predictors via random permutation of each predictor one at a time and calculating the associated reduction in predictive performance. This experimental measure 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).

See also

summary.GBMFit Random Forests.

Author

Greg Ridgeway gregridgeway@gmail.com