Example with random forest model

1 Dataset

We work on Apartments dataset from DALEX package.

#>   m2.price construction.year surface floor no.rooms    district
#> 1     5897              1953      25     3        1 Srodmiescie
#> 2     1818              1992     143     9        5     Bielany
#> 3     3643              1937      56     1        2       Praga
#> 4     3517              1995      93     7        3      Ochota
#> 5     3013              1992     144     6        5     Mokotow
#> 6     5795              1926      61     6        2 Srodmiescie

2 Random forest regression model

Now, we define a random forest regression model and use explain from DALEX.

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 
#>   -> model_info        :  package randomForest , ver. 4.6.14 , task regression (  default  ) 
#>   -> predict function  :  yhat.randomForest  will be used (  default  )
#>   -> predicted values  :  numerical, min =  2096.568 , mean =  3514.335 , max =  5350.168  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -1328.848 , mean =  -2.811296 , max =  2164.468  
#>   A new explainer has been created! 

3 New observation

We need to specify an observation. Let consider a new apartment with following attributes. Moreover, we calculate predict value for this new observation.

new_apartment <- data.frame(construction.year = 1998, surface = 88, floor = 2L, no.rooms = 3)
predict(apartments_rf_model, new_apartment)
#>        1 
#> 3882.104

4 Calculate Ceteris Paribus profiles

Let see the Ceteris Paribus Plots calculated with ceteris_paribus() function.

library("ingredients")
profiles <- ingredients::ceteris_paribus(explainer_rf, new_apartment)
plot(profiles) + show_observations(profiles)

5 Calculate measure of local variable importance

Now, we calculated a measure of local variable importance via oscillation based on Ceteris Paribus plot. We use variant with all parameters equals to TRUE.

library("vivo")
measure <- local_variable_importance(profiles, apartments[,2:5], 
            absolute_deviation = TRUE, point = TRUE, density = TRUE)

plot(measure)

For the new observation the most important variable is surface, then floor, construction.year and no.rooms.