View source: R/permutation-relative-influence.r
permutation_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.
permutation_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 |
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).
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.
Greg Ridgeway gregridgeway@gmail.com
summary.GBMFit
Random
Forests.
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