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)