Principal Component Analysis of models

Principal Component Analysis of models residuals. PCA can be used to assess the similarity of the models.

plot_pca(object, ..., scale = TRUE, arrow_size = 2)

plotModelPCA(object, ..., scale = TRUE)

Arguments

object	An object of class auditor_model_residual created with model_residual function.
...	Other auditor_model_residual objects to be plotted together.
scale	A logical value indicating whether the models residuals should be scaled before the analysis.
arrow_size	Width of the arrows.

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
mr_lm <- model_residual(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 =  778.2469 , mean =  1341.443 , max =  2484.578  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -190.0118 , mean =  6.344159 , max =  413.8431  
#>   A new explainer has been created!  
mr_rf <- model_residual(rf_audit)

# plot results
plot_pca(mr_lm, mr_rf)