Receiver Operating Characteristic (ROC) in D3 with r2d3 package.

Receiver Operating Characteristic Curve is a plot of the true positive rate (TPR) against the false positive rate (FPR) for the different thresholds. It is useful for measuring and comparing the accuracy of the classificators.

plotD3_roc(object, ..., nlabel = NULL, scale_plot = FALSE)

Arguments

object	An object of class auditor_model_evaluation created with model_evaluation function.
...	Other auditor_model_evaluation objects to be plotted together.
nlabel	Number of cutoff points to show on the plot. Default is NULL.
scale_plot	Logical, indicates whenever the plot should scale with height. By default it's FALSE.

Value

a r2d3 object

See also

plot_roc

Examples

data(titanic_imputed, package = "DALEX")

# fit a model
model_glm <- glm(survived ~ ., family = binomial, data = titanic_imputed)

# use DALEX package to wrap up a model into explainer
glm_audit <- audit(model_glm,
                   data = titanic_imputed,
                   y = titanic_imputed$survived)
#> Preparation of a new explainer is initiated
#>   -> model label       :  lm  (  default  )
#>   -> data              :  2207  rows  8  cols 
#>   -> target variable   :  2207  values 
#>   -> predict function  :  yhat.glm  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package stats , ver. 4.1.1 , task classification (  default  ) 
#>   -> predicted values  :  numerical, min =  0.008128381 , mean =  0.3221568 , max =  0.9731431  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.9628583 , mean =  -2.569729e-10 , max =  0.9663346  
#>   A new explainer has been created!  

# validate a model with auditor
eva_glm <- model_evaluation(glm_audit)

# plot results
plot_roc(eva_glm)
plot(eva_glm)

#add second model
model_glm_2 <- glm(survived ~ .-age, family = binomial, data = titanic_imputed)
glm_audit_2 <- audit(model_glm_2,
                     data = titanic_imputed,
                     y = titanic_imputed$survived,
                     label = "glm2")
#> Preparation of a new explainer is initiated
#>   -> model label       :  glm2 
#>   -> data              :  2207  rows  8  cols 
#>   -> target variable   :  2207  values 
#>   -> predict function  :  yhat.glm  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package stats , ver. 4.1.1 , task classification (  default  ) 
#>   -> predicted values  :  numerical, min =  0.01618585 , mean =  0.3221568 , max =  0.9628841  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.9354237 , mean =  -1.981381e-10 , max =  0.9654417  
#>   A new explainer has been created!  
eva_glm_2 <- model_evaluation(glm_audit_2)

plotD3_roc(eva_glm, eva_glm_2)