Model Ranking Plot

Radar plot with model score. score are scaled to [0,1], each score is inversed and divided by maximum score value.

plot_radar(object, ..., verbose = TRUE)

plotModelRanking(object, ..., verbose = TRUE)

Arguments

object	An object of class auditor_model_performance created with model_performance function.
...	Other auditor_model_performance objects to be plotted together.
verbose	Logical, indicates whether values of scores should be printed.

Value

A ggplot object.

Examples

dragons <- DALEX::dragons[1:100, ]

# fit a model
model_lm <- lm(life_length ~ ., data = dragons)

lm_audit <- audit(model_lm, data = dragons, y = dragons$life_length)
#> Preparation of a new explainer is initiated
#>   -> model label       :  lm  (  default  )
#>   -> data              :  100  rows  8  cols 
#>   -> target variable   :  100  values 
#>   -> predict function  :  yhat.lm  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package stats , ver. 4.1.1 , task regression (  default  ) 
#>   -> predicted values  :  numerical, min =  585.8311 , mean =  1347.787 , max =  2942.307  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -88.41755 , mean =  -1.489291e-13 , max =  77.92805  
#>   A new explainer has been created!  

# validate a model with auditor
mp_lm <- model_performance(lm_audit)


library(randomForest)
model_rf <- randomForest(life_length~., data = dragons)
rf_audit <- audit(model_rf, data = dragons, y = dragons$life_length)
#> Preparation of a new explainer is initiated
#>   -> model label       :  randomForest  (  default  )
#>   -> data              :  100  rows  8  cols 
#>   -> target variable   :  100  values 
#>   -> predict function  :  yhat.randomForest  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package randomForest , ver. 4.6.14 , task regression (  default  ) 
#>   -> predicted values  :  numerical, min =  763.6184 , mean =  1342.852 , max =  2458.261  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -178.9473 , mean =  4.934734 , max =  440.1598  
#>   A new explainer has been created!  
mp_rf <- model_performance(rf_audit)

# plot results
plot_radar(mp_lm, mp_rf)
#> Error in check_object(object, type = "prfm"): The function requires an object created with function model_performance(). Please, see the current workflow in the paper https://arxiv.org/abs/1809.07763