Print Explainer Summary
# S3 method for explainer
print(x, ...)a model explainer created with the `explain` function
other parameters
aps_lm_model4 <- lm(m2.price~., data = apartments)
aps_lm_explainer4 <- explain(aps_lm_model4, data = apartments, y = apartments$m2.price,
label = "model_4v")
#> Preparation of a new explainer is initiated
#> -> model label : model_4v
#> -> data : 1000 rows 6 cols
#> -> target variable : 1000 values
#> -> predict function : yhat.lm will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package stats , ver. 4.2.3 , task regression ( default )
#> -> predicted values : numerical, min = 1781.848 , mean = 3487.019 , max = 6176.032
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -247.4728 , mean = 2.093656e-14 , max = 469.0023
#> A new explainer has been created!
aps_lm_explainer4
#> Model label: model_4v
#> Model class: lm
#> Data head :
#> m2.price construction.year surface floor no.rooms district
#> 1 5897 1953 25 3 1 Srodmiescie
#> 2 1818 1992 143 9 5 Bielany
# \donttest{
library("ranger")
titanic_ranger_model <- ranger(survived~., data = titanic_imputed, num.trees = 50,
probability = TRUE)
explainer_ranger <- explain(titanic_ranger_model, data = titanic_imputed[,-8],
y = titanic_imputed$survived,
label = "model_ranger")
#> Preparation of a new explainer is initiated
#> -> model label : model_ranger
#> -> data : 2207 rows 7 cols
#> -> target variable : 2207 values
#> -> predict function : yhat.ranger will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package ranger , ver. 0.14.1 , task classification ( default )
#> -> predicted values : numerical, min = 0.007549603 , mean = 0.3235824 , max = 0.9959444
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -0.7983078 , mean = -0.001425674 , max = 0.8726193
#> A new explainer has been created!
explainer_ranger
#> Model label: model_ranger
#> Model class: ranger
#> Data head :
#> gender age class embarked fare sibsp parch
#> 1 male 42 3rd Southampton 7.11 0 0
#> 2 male 13 3rd Southampton 20.05 0 2
# }