Score is approximately: \( \sum{\#[res_i \leq simres_{i,j}] - n } \) with the distinction that each element of sum is also scaled to take values from [0,1].
\(res_i\) is a residual for i-th observation, \(simres_{i,j}\) is the residual of j-th simulation for i-th observation, and \(n\) is the number of simulations for each observation. Scores are calculated on the basis of simulated data, so they may differ between function calls.
score_halfnormal(object, ...) scoreHalfNormal(object, ...)
| object | An object of class |
|---|---|
| ... | ... |
An object of class auditor_score.
dragons <- DALEX::dragons[1:100, ] # fit a model model_lm <- lm(life_length ~ ., data = dragons) # create an explainer 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_halfnormal(lm_audit)#> Gaussian model (lm object)#> halfnormal: NA