print.break_down_uncertainty: Print Generic for Break Down Uncertainty Objects

In iBreakDown: Model Agnostic Instance Level Variable Attributions

Print Generic for Break Down Uncertainty Objects

Description

Print Generic for Break Down Uncertainty Objects

Usage

## S3 method for class 'break_down_uncertainty'
print(x, ...)

Arguments

x	an explanation created with break_down_uncertainty
...	other parameters.

Value

a data frame.

References

Explanatory Model Analysis. Explore, Explain and Examine Predictive Models. https://ema.drwhy.ai

Examples

library("DALEX")
library("iBreakDown")
set.seed(1313)
model_titanic_glm <- glm(survived ~ gender + age + fare,
                       data = titanic_imputed, family = "binomial")
explain_titanic_glm <- explain(model_titanic_glm,
                           data = titanic_imputed,
                           y = titanic_imputed$survived,
                           label = "glm")

bd_glm <- break_down_uncertainty(explain_titanic_glm, titanic_imputed[1, ])
bd_glm
plot(bd_glm)

## Not run: 
## Not run:
library("randomForest")
set.seed(1313)
model <- randomForest(status ~ . , data = HR)
new_observation <- HR_test[1,]

explainer_rf <- explain(model,
                        data = HR[1:1000,1:5],
                        y = HR$status[1:1000],
                        verbose = FALSE)

bd_rf <- break_down_uncertainty(explainer_rf,
                           new_observation)
bd_rf

# example for regression - apartment prices
# here we do not have intreactions
model <- randomForest(m2.price ~ . , data = apartments)
explainer_rf <- explain(model,
                        data = apartments_test[1:1000,2:6],
                        y = apartments_test$m2.price[1:1000])

bd_rf <- break_down_uncertainty(explainer_rf, apartments_test[1,])
bd_rf

## End(Not run)

iBreakDown documentation built on May 29, 2024, 10:38 a.m.