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Variable Interactions

Source: R/orsf_vint.R
orsf_vint.Rd

Use the variable interaction score described in Greenwell et al (2018). As this method can be computationally demanding, using n_thread=0 can substantially reduce time needed to compute scores.

Usage

orsf_vint(
  object,
  predictors = NULL,
  n_thread = NULL,
  verbose_progress = NULL,
  sep = ".."
)

Arguments

object

(ObliqueForest) a trained oblique random forest object (see orsf)

predictors

(character) a vector of length 2 or more with names of predictors used by object. All pairwise interactions between the predictors will be scored. If NULL (the default), all predictors are used.

n_thread

(integer) number of threads to use while growing trees, computing predictions, and computing importance. Default is 0, which allows a suitable number of threads to be used based on availability.

verbose_progress

(logical) if TRUE, progress messages are printed in the console. If FALSE (the default), nothing is printed.

sep

(character) how to separate the names of two predictors. The default value of ".." returns names as name1..name2

Value

a data.table with variable interaction scores and partial dependence values.

Details

The number of possible interactions grows exponentially based on the number of predictors. Some caution is warranted when using large predictor sets and it is recommended that you supply a specific vector of predictor names to assess rather than a global search. A good strategy is to use n_tree = 5 to search all predictors, then pick the top 10 interactions, get the unique predictors from them, and re-run on just those predictors with more trees.

References

  1. Greenwell, M B, Boehmke, C B, McCarthy, J A (2018). "A simple and effective model-based variable importance measure." arXiv preprint arXiv:1805.04755.

Examples


set.seed(329)

data <- data.frame(
 x1 = rnorm(500),
 x2 = rnorm(500),
 x3 = rnorm(500)
)

data$y = with(data, expr = x1 + x2 + x3 + 1/2*x1 * x2 + x2 * x3 + rnorm(500))

forest <- orsf(data, y ~ ., n_tree = 5)

orsf_vint(forest)
#>    interaction     score          pd_values
#>         <char>     <num>             <list>
#> 1:      x2..x3 0.8021932 <data.table[25x9]>
#> 2:      x1..x2 0.5095065 <data.table[25x9]>
#> 3:      x1..x3 0.1133252 <data.table[25x9]>