One minus accuracy

score_one_minus_acc(object, cutoff = 0.5, data = NULL, y = NULL, ...)

Arguments

object	An object of class `explainer` created with function `explain` from the DALEX package.
cutoff	Threshold value, which divides model predicted values to calculate confusion matrix. By default it's `0.5`.
data	New data that will be used to calculate the score. Pass `NULL` if you want to use `data` from `object`.
y	New y parameter will be used to calculate score.
...	Other arguments dependent on the type of score.

Value

An object of class auditor_score.

Examples

data(titanic_imputed, package = "DALEX")

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

# create an 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!  

# calculate score
score_one_minus_acc(glm_audit)
#> one_minus_acc: 0.1998188