The R2 is the coefficient of determination, An R2 coefficient equals 0 means that model explains none of the variability of the response. An R2 coefficient equals 1 means that model explains all the variability of the response.
score_r2(object, data = NULL, y = NULL, ...)
| object | An object of class |
|---|---|
| data | New data that will be used to calculate the score.
Pass |
| y | New y parameter will be used to calculate score. |
| ... | Other arguments dependent on the type of score. |
An object of class auditor_score.
dragons <- DALEX::dragons[1:100, ] # fit a model model_lm <- lm(life_length ~ ., data = dragons) # use DALEX package to wrap up a model into 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 with auditor score_r2(lm_audit)#> r2: 0.9925428