Function allows users to update data an y of any explainer in a unified way. It doesn't require knowledge about structre of an explainer.

update_data(explainer, data, y = NULL, verbose = TRUE)

Arguments

explainer

- explainer object that is supposed to be updated.

data

- new data, is going to be passed to an explainer

y

- new y, is going to be passed to an explainer

verbose

- logical, indicates if information about update should be printed

Value

updated explainer object

Examples

aps_lm_model4 <- lm(m2.price ~., data = apartments) aps_lm_explainer4 <- explain(aps_lm_model4, data = apartments, label = "model_4v")
#> Preparation of a new explainer is initiated #> -> model label : model_4v #> -> data : 1000 rows 6 cols #> -> target variable : not specified! ( WARNING ) #> -> 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 ) #> -> model_info : Model info detected regression task but 'y' is a NULL . ( WARNING ) #> -> model_info : By deafult regressions tasks supports only numercical 'y' parameter. #> -> model_info : Consider changing to numerical vector. #> -> model_info : Otherwise I will not be able to calculate residuals or loss function. #> -> predicted values : numerical, min = 1781.848 , mean = 3487.019 , max = 6176.032 #> -> residual function : difference between y and yhat ( default ) #> A new explainer has been created!
explainer <- update_data(aps_lm_explainer4, data = apartmentsTest, y = apartmentsTest$m2.price)
#> -> data : 9000 rows 6 cols #> -> target variable : 9000 values #> An explainer has been updated!