Model Scores computations

This function provides several scores for model validation and performance assessment. Scores can be also used to compare models.

score(object, type = "mse", data = NULL, ...)

Arguments

object	An object of class explainer created with function explain from the DALEX package.
type	The score to be calculated. Possible values: acc, auc, cookdistance, dw, f1, gini, halfnormal, mae, mse, peak, precision, r2, rec, recall, rmse, rroc, runs, specificity, one_minus_acc, one_minus_auc, one_minus_f1, one_minus_gini, one_minus_precision, one_minus_recall, one_minus_specificity (for detailed description see functions in see also section).
data	New data that will be used to calculate the score. Pass NULL if you want to use data from object.
...	Other arguments dependent on the type of score.

Value

An object of class auditor_score, except Cooks distance, where numeric vector is returned.

See also

score_acc, score_auc, score_cooksdistance, score_dw, score_f1, score_gini score_halfnormal, score_mae, score_mse, score_peak, score_precision, score_r2, score_rec, score_recall, score_rmse, score_rroc, score_runs, score_specificity, score_one_minus_acc, score_one_minus_auc, score_one_minus_f1, score_one_minus_gini, score_one_minus_precision, score_one_minus_recall, score_one_minus_specificity

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!  

# calculate score
score(lm_audit, type = 'mae')
#> mae: 31.81926