safely_detect_interactions: Detecting Interactions via Permutation Approach

In ModelOriented/rSAFE: Surrogate-Assisted Feature Extraction

Detecting Interactions via Permutation Approach

Description

The safely_detect_interactions() function detects second-order interactions based on predictions made by a surrogate model. For each pair of features it performs values permutation in order to evaluate their non_additive effect.

Usage

safely_detect_interactions(
  explainer,
  inter_param = 0.5,
  inter_threshold = 0.5,
  verbose = TRUE
)

Arguments

explainer	DALEX explainer created with explain() function
inter_param	numeric, a positive value indicating which of single observation non-additive effects are to be regarded as significant, the higher value the higher non-additive effect has to be to be taken into account
inter_threshold	numeric, a value from [0,1] interval indicating which interactions should be returned as significant. It corresponds to the percentage of observations for which interaction measure is greater than inter_param - if this percentage is less than inter_threshold then interaction effect is ignored.
verbose	logical, if progress bar is to be printed

Value

dataframe object containing interactions effects greater than or equal to the specified inter_threshold

See Also

safe_extraction

Examples

library(DALEX)
library(randomForest)
library(rSAFE)

data <- apartments[1:500,]
set.seed(111)
model_rf <- randomForest(m2.price ~ construction.year + surface + floor +
                           no.rooms + district, data = data)
explainer_rf <- explain(model_rf, data = data[,2:6], y = data[,1])
safely_detect_interactions(explainer_rf, inter_param = 0.25,
                          inter_threshold = 0.2, verbose = TRUE)

ModelOriented/rSAFE documentation built on Aug. 19, 2022, 2:54 a.m.