Plot function for overall_comparison

The function plots data created with overall_comparison. For radar plot it uses auditor's plot_radar. Keep in mind that the function creates two plots returned as list.

# S3 method for overall_comparison
plot(x, ...)

Arguments

x

- data created with overall_comparison

...

- other parameters

Value

A named list of ggplot objects. It consists of:

• radar_plot plot created with plot_radar

• accordance_plot accordance plot of responses. OX axis stand for champion response, while OY for one of challengers responses. Colour indicates on challenger.

Examples

# \donttest{
library("DALEXtra")
library("mlr")
task <- mlr::makeRegrTask(
  id = "R",
  data = apartments,
  target = "m2.price"
)
learner_lm <- mlr::makeLearner(
  "regr.lm"
)
model_lm <- mlr::train(learner_lm, task)
explainer_lm <- explain_mlr(model_lm, apartmentsTest, apartmentsTest$m2.price, label = "LM")
#> Preparation of a new explainer is initiated
#>   -> model label       :  LM 
#>   -> data              :  9000  rows  6  cols 
#>   -> target variable   :  9000  values 
#>   -> predict function  :  yhat.WrappedModel  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package mlr , ver. 2.19.0 , task regression (  default  ) 
#>   -> predicted values  :  numerical, min =  1792.597 , mean =  3506.836 , max =  6241.447  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -257.2555 , mean =  4.687686 , max =  472.356  
#>   A new explainer has been created!  

learner_rf <- mlr::makeLearner(
  "regr.ranger"
)
model_rf <- mlr::train(learner_rf, task)
explainer_rf <- explain_mlr(model_rf, apartmentsTest, apartmentsTest$m2.price, label = "RF")
#> Preparation of a new explainer is initiated
#>   -> model label       :  RF 
#>   -> data              :  9000  rows  6  cols 
#>   -> target variable   :  9000  values 
#>   -> predict function  :  yhat.WrappedModel  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package mlr , ver. 2.19.0 , task regression (  default  ) 
#>   -> predicted values  :  numerical, min =  1798.643 , mean =  3503.859 , max =  6221.87  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -529.6339 , mean =  7.664423 , max =  759.7997  
#>   A new explainer has been created!  

learner_gbm <- mlr::makeLearner(
  "regr.gbm"
)
model_gbm<- mlr::train(learner_gbm, task)
explainer_gbm <- explain_mlr(model_gbm, apartmentsTest, apartmentsTest$m2.price, label = "GBM")
#> Preparation of a new explainer is initiated
#>   -> model label       :  GBM 
#>   -> data              :  9000  rows  6  cols 
#>   -> target variable   :  9000  values 
#>   -> predict function  :  yhat.WrappedModel  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package mlr , ver. 2.19.0 , task regression (  default  ) 
#>   -> predicted values  :  numerical, min =  2112.462 , mean =  3505.275 , max =  6046.481  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -509.462 , mean =  6.248255 , max =  724.3794  
#>   A new explainer has been created!  

data <- overall_comparison(explainer_lm, list(explainer_gbm, explainer_rf), type = "regression")
plot(data)
#> $radar_plot

#> 
#> $accordance_plot

#> 
# }