R/local_variable_importance.R
local_variable_importance.Rd
This function calculate local importance measure in eight variants. We obtain eight variants measure through the possible options of three parameters such as absolute_deviation
, point
and density
.
local_variable_importance( profiles, data, absolute_deviation = TRUE, point = TRUE, density = TRUE, grid_points = 101 )
profiles 


data 

absolute_deviation  logical parameter, if 
point  logical parameter, if 
density  logical parameter, if 
grid_points  maximum number of points for profile calculations, the default values is 101, the same as in 
A data.frame
of the class local_variable_importance
.
It's a data.frame
with calculated local variable importance measure.
library("DALEX") data(apartments) library("randomForest") apartments_rf_model < randomForest(m2.price ~ construction.year + surface + floor + no.rooms, data = apartments) explainer_rf < explain(apartments_rf_model, data = apartmentsTest[,2:5], y = apartmentsTest$m2.price)#> Preparation of a new explainer is initiated #> > model label : randomForest ( default ) #> > data : 9000 rows 4 cols #> > target variable : 9000 values #> > predict function : yhat.randomForest will be used ( default ) #> > predicted values : numerical, min = 2088.426 , mean = 3515.222 , max = 5384.985 #> > model_info : package randomForest , ver. 4.6.14 , task regression ( default ) #> > residual function : difference between y and yhat ( default ) #> > residuals : numerical, min = 1277.025 , mean = 3.698933 , max = 2085.661 #> A new explainer has been created!new_apartment < data.frame(construction.year = 1998, surface = 88, floor = 2L, no.rooms = 3) profiles < predict_profile(explainer_rf, new_apartment) library("vivo") local_variable_importance(profiles, apartments[,2:5], absolute_deviation = TRUE, point = TRUE, density = TRUE)#> variable_name measure _label_model_ #> 1 construction.year 179.61607 randomForest #> 2 surface 287.59996 randomForest #> 3 floor 274.95305 randomForest #> 4 no.rooms 64.27073 randomForest #> _label_method_ #> 1 absolute_deviation = TRUE, point = TRUE, density = TRUE #> 2 absolute_deviation = TRUE, point = TRUE, density = TRUE #> 3 absolute_deviation = TRUE, point = TRUE, density = TRUE #> 4 absolute_deviation = TRUE, point = TRUE, density = TRUElocal_variable_importance(profiles, apartments[,2:5], absolute_deviation = TRUE, point = TRUE, density = FALSE)#> variable_name measure _label_model_ #> 1 construction.year 178.67763 randomForest #> 2 surface 283.38958 randomForest #> 3 floor 257.88508 randomForest #> 4 no.rooms 59.82041 randomForest #> _label_method_ #> 1 absolute_deviation = TRUE, point = TRUE, density = FALSE #> 2 absolute_deviation = TRUE, point = TRUE, density = FALSE #> 3 absolute_deviation = TRUE, point = TRUE, density = FALSE #> 4 absolute_deviation = TRUE, point = TRUE, density = FALSElocal_variable_importance(profiles, apartments[,2:5], absolute_deviation = TRUE, point = FALSE, density = TRUE)#> variable_name measure _label_model_ #> 1 construction.year 132.73394 randomForest #> 2 surface 299.56126 randomForest #> 3 floor 212.61949 randomForest #> 4 no.rooms 66.79469 randomForest #> _label_method_ #> 1 absolute_deviation = TRUE, point = FALSE, density = TRUE #> 2 absolute_deviation = TRUE, point = FALSE, density = TRUE #> 3 absolute_deviation = TRUE, point = FALSE, density = TRUE #> 4 absolute_deviation = TRUE, point = FALSE, density = TRUE