R/misc_loss_functions.R
get_loss_yardstick.RdThe yardstick package provides many auxiliary functions for calculating the predictive performance of the model. However, they have an interface that is consistent with the tidyverse philosophy. The loss_yardstick function adapts loss functions from the yardstick package to functions understood by DALEX. Type compatibility for y-values and for predictions must be guaranteed by the user.
get_loss_yardstick(loss, reverse = FALSE, reference = 1)
loss_yardstick(loss, reverse = FALSE, reference = 1)loss function from the yardstick package
shall the metric be reversed? for loss metrics lower values are better. reverse = TRUE is useful for accuracy-like metrics
if the metric is reverse then it is calculated as reference - loss. The default value is 1.
loss function that can be used in the model_parts function
# \donttest{
titanic_glm_model <- glm(survived~., data = titanic_imputed, family = "binomial")
explainer_glm <- DALEX::explain(titanic_glm_model,
data = titanic_imputed[,-8],
y = factor(titanic_imputed$survived))
#> Preparation of a new explainer is initiated
#> -> model label : lm ( default )
#> -> data : 2207 rows 7 cols
#> -> target variable : 2207 values
#> -> predict function : yhat.glm will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package stats , ver. 4.2.3 , task classification ( default )
#> -> model_info : Model info detected classification task but 'y' is a factor . ( WARNING )
#> -> model_info : By deafult classification tasks supports only numercical 'y' parameter.
#> -> model_info : Consider changing to numerical vector with 0 and 1 values.
#> -> model_info : Otherwise I will not be able to calculate residuals or loss function.
#> -> predicted values : numerical, min = 0.008128381 , mean = 0.3221568 , max = 0.9731431
#> -> residual function : difference between y and yhat ( default )
#> Warning: ‘-’ not meaningful for factors
#> -> residuals : numerical, min = NA , mean = NA , max = NA
#> A new explainer has been created!
# See the 'How to use DALEX with the yardstick package' vignette
# which explains this model with measures implemented in the 'yardstick' package
# }