break_down_uncertainty: Explanation Level Uncertainty of Sequential Variable...

In iBreakDown: Model Agnostic Instance Level Variable Attributions

Explanation Level Uncertainty of Sequential Variable Attribution

Description

This function calculates the break down algorithm for B random orderings. Then it calculates the distribution of attributions for these different orderings. Note that the shap() function is just a simplified interface to the break_down_uncertainty() function with a default value set to B=25.

Usage

break_down_uncertainty(x, ..., keep_distributions = TRUE, B = 10)

## S3 method for class 'explainer'
break_down_uncertainty(
  x,
  new_observation,
  ...,
  keep_distributions = TRUE,
  B = 10
)

## Default S3 method:
break_down_uncertainty(
  x,
  data,
  predict_function = predict,
  new_observation,
  label = class(x)[1],
  ...,
  path = NULL,
  keep_distributions = TRUE,
  B = 10
)

shap(x, ..., B = 25)

Arguments

x	an explainer created with function explain or a model.
...	other parameters.
keep_distributions	if TRUE then we will keep distribution for predicted values. It's needed by the describe function.
B	number of random paths
new_observation	a new observation with columns that correspond to variables used in the model.
data	validation dataset, will be extracted from x if it is an explainer.
predict_function	predict function, will be extracted from x if it is an explainer.
label	name of the model. By default it's extracted from the 'class' attribute of the model.
path	if specified, then this path will be highlighed on the plot. Use average in order to show an average effect

Value

an object of the break_down_uncertainty class.

References

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

See Also

break_down, local_attributions

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")

# there is no explanation level uncertanity linked with additive models
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])

bd_rf <- break_down_uncertainty(explainer_rf,
                           new_observation)
bd_rf
plot(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
plot(bd_rf)

bd_rf <- break_down_uncertainty(explainer_rf, apartments_test[1,], path = 1:5)
plot(bd_rf)

bd_rf <- break_down_uncertainty(explainer_rf,
                                     apartments_test[1,],
                                     path = c("floor", "no.rooms", "district",
                                         "construction.year", "surface"))
plot(bd_rf)

bd <- break_down(explainer_rf,
                    apartments_test[1,])
plot(bd)

s <- shap(explainer_rf,
                   apartments_test[1,])
plot(s)

## End(Not run)

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