Extract the active features from a tree

If a tree does not use a predictor in the training set in any of its splits it is functionally independent of the prediction function. This generic returns a data frame containing character vector of predictor names that were used in at least one split.

Usage

# S3 method for class 'C5.0'
active_predictors(x, tree = 1L, ...)

# S3 method for class 'ObliqueForest'
active_predictors(x, tree = 1L, ...)

# S3 method for class 'bart'
active_predictors(x, tree = 1L, ...)

# S3 method for class 'cforest'
active_predictors(x, tree = 1L, ...)

# S3 method for class 'cubist'
active_predictors(x, ...)

active_predictors(x, ...)

# S3 method for class 'grf'
active_predictors(x, tree = 1L, ...)

# S3 method for class 'lgb.Booster'
active_predictors(x, tree = 1L, ...)

# S3 method for class 'party'
active_predictors(x, ...)

# S3 method for class 'randomForest'
active_predictors(x, tree = 1L, ...)

# S3 method for class 'ranger'
active_predictors(x, tree = 1L, ...)

# S3 method for class 'rpart'
active_predictors(x, ...)

# S3 method for class 'xgb.Booster'
active_predictors(x, tree = 1L, nthread = NULL, ...)

Arguments

x

A object

tree

Integer vector specifying which trees to extract active predictors from. Default is 1L for the first tree. Values must be between 1 and the number of trees in the forest.

...

Other arguments passed to methods

nthread

Integer number of threads to use when reading the tree structure out of an xgboost model. The default (NULL) inherits the nthread the booster was trained with.

Value

A tibble with list column active_predictors containing a character vector of predictors.

Examples

if (rlang::is_installed(c("rpart", "palmerpenguins"))) {
  data(penguins, package = "palmerpenguins")
  penguins <- na.omit(penguins)

  # Fit a tree
  tree <- rpart::rpart(species ~ ., data = penguins)
  tree

  # Extract active predictors
  active_predictors(tree)

  # Only primary splits are included - competing and surrogate splits
  # are excluded since they don't affect predictions
}
#> # A tibble: 1 × 1
#>   active_predictors
#>   <list>           
#> 1 <chr [3]>        

# C5.0 single tree
if (rlang::is_installed(c("C50", "palmerpenguins"))) {
  data(penguins, package = "palmerpenguins")
  penguins <- na.omit(penguins)

  # Tree-based model
  c5_tree <- C50::C5.0(species ~ ., data = penguins)
  active_predictors(c5_tree)

  # Boosted model - extract from multiple trials
  c5_boost <- C50::C5.0(species ~ ., data = penguins, trials = 5)
  active_predictors(c5_boost, tree = 1:3)

  # Rule-based model
  c5_rules <- C50::C5.0(species ~ ., data = penguins, rules = TRUE)
  active_predictors(c5_rules)
}
#> Registered S3 method overwritten by 'C50':
#>   method        from 
#>   as.party.C5.0 lorax
#> # A tibble: 1 × 1
#>   active_predictors
#>   <list>           
#> 1 <chr [4]>