vivo (version 0.2.1)

local_variable_importance: Local Variable Importance measure based on Ceteris Paribus profiles.

Description

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.

Usage

local_variable_importance(
  profiles,
  data,
  absolute_deviation = TRUE,
  point = TRUE,
  density = TRUE,
  grid_points = 101
)

Arguments

profiles

data.frame generated by DALEX::predict_profile(), DALEX::individual_profile() or ingredients::ceteris_paribus()

data

data.frame with raw data to model

absolute_deviation

logical parameter, if absolute_deviation = TRUE then measure is calculated as absolute deviation, else is calculated as a root from average squares

point

logical parameter, if point = TRUE then measure is calculated as a distance from f(x), else measure is calculated as a distance from average profiles

density

logical parameter, if density = TRUE then measure is weighted based on the density of variable, else is not weighted

grid_points

maximum number of points for profile calculations, the default values is 101, the same as in ingredients::ceteris_paribus(), if you use a different on, you should also change here

Value

A data.frame of the class local_variable_importance. It's a data.frame with calculated local variable importance measure.

Examples

# NOT RUN {

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)

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)

local_variable_importance(profiles, apartments[,2:5],
                          absolute_deviation = TRUE, point = TRUE, density = FALSE)

local_variable_importance(profiles, apartments[,2:5],
                          absolute_deviation = TRUE, point = FALSE, density = TRUE)



# }