DALEX (version 2.4.1)

loss_yardstick: Wrapper for Loss Functions from the Yarstick Package

Description

The 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.

Usage

loss_yardstick(loss)

Arguments

loss

loss function from the yardstick package

Value

loss function that can be used in the model_parts function

Examples

# NOT RUN {
 
# }
# NOT RUN {
 # Classification Metrics
 # y and y_hat are factors!!!
 library("yardstick")

 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),
                                 predict_function = function(m, x) {
                                   factor((predict(m, x, type = "response") > 0.5) + 0)
                                 })

model_parts_accuracy <- model_parts(explainer_glm, type = "raw",
                             loss_function = loss_yardstick(accuracy))
plot(model_parts_accuracy)

# Class Probability Metrics
# y is a factor while y_hat is a numeric!!!

 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))

model_parts_accuracy <- model_parts(explainer_glm, type = "raw",
                             loss_function = loss_yardstick(roc_auc))
plot(model_parts_accuracy)

# Regression Metrics
# y and y_hat are numeric!!!

library("ranger")
apartments_ranger <- ranger(m2.price~., data = apartments, num.trees = 50)
explainer_ranger  <- DALEX::explain(apartments_ranger, data = apartments[,-1],
                                    y = apartments$m2.price, label = "Ranger Apartments")
model_parts_ranger <- model_parts(explainer_ranger, type = "raw",
                       loss_function = loss_yardstick(rsq))
plot(model_parts_ranger)

# }
# NOT RUN {
# }