Plot Global Variable Importance measure — plot.global

Function plot.global_importance plots global importance measure based on Partial Dependence profiles.

# S3 method for global_importance
plot(x, ..., variables = NULL, type = NULL, title = "Variable importance")

Arguments

x	object returned from `global_variable_importance()` function
...	other object returned from `global_variable_importance()` function that shall be plotted together
variables	if not `NULL` then only `variables` will be presented
type	a character. How variables shall be plotted? Either "bars" (default) or "lines".
title	the plot's title, by default `'Variable importance'`

Value

a ggplot2 object

Examples


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 =  2106.103 , mean =  3516.09 , max =  5283.956  
#>   -> model_info        :  package randomForest , ver. 4.6.14 , task regression (  default  ) 
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -1260.225 , mean =  -4.566031 , max =  2160.703  
#>   A new explainer has been created!  

profiles <- model_profile(explainer_rf)

library("vivo")
measure <- global_variable_importance(profiles)

plot(measure)