iBreakDown (version 0.9.6)

print.break_down_uncertainty: Print Generic for Break Down Uncertainty Objects

Description

Print Generic for Break Down Uncertainty Objects

Usage

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

Arguments

x

object of `break_down_uncertainty` class.

...

other parameters.

Value

a data frame.

References

Predictive Models: Visual Exploration, Explanation and Debugging https://pbiecek.github.io/PM_VEE

Examples

# NOT RUN {
library("DALEX")
library("iBreakDown")
# Toy examples, because CRAN angels ask for them
titanic <- na.omit(titanic)
set.seed(1313)
titanic_small <- titanic[sample(1:nrow(titanic), 500), c(1,2,6,9)]
model_titanic_glm <- glm(survived == "yes" ~ gender + age + fare,
                       data = titanic_small, family = "binomial")
explain_titanic_glm <- explain(model_titanic_glm,
                           data = titanic_small[,-9],
                           y = titanic_small$survived == "yes")
bd_rf <- break_down_uncertainty(explain_titanic_glm, titanic_small[1, ])
bd_rf
plot(bd_rf)

# }
# 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])

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
# }