Create model evaluation explanation

Creates explanation of classification model.

Returns, among others, true positive rate (tpr), false positive rate (fpr), rate of positive prediction (rpp), and true positives (tp).

Created object of class auditor_model_evaluation can be used to plot Receiver Operating Characteristic (ROC) curve (plot plot_roc) and LIFT curve (plot plot_lift).

model_evaluation(object)

modelEvaluation(object)

Arguments

object	An object of class explainer created with function explain from the DALEX package.

Value

An object of the class auditor_model_evaluation.

Examples

data(titanic_imputed, package = "DALEX")

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

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
me <- model_evaluation(glm_audit)
me
#> Model label:  lm 
#> 
#>  True Positive Rate for cutoff 0.5: 0 
#> 
#>  False Positive Rate for cutoff 0.5: 0 

plot(me)