break_down: Model Agnostic Sequential Variable Attributions

Description

This function finds Variable Attributions via Sequential Variable Conditioning. It calls either local_attributions for additive attributions or local_interactions for attributions with interactions.

Usage

break_down(x, ..., interactions = FALSE)
# S3 method for explainer
break_down(x, new_observation, ...,
  interactions = FALSE)
# S3 method for default
break_down(x, data, predict_function = predict,
  new_observation, keep_distributions = FALSE, order = NULL,
  label = class(x)[1], ..., interactions = interactions)

Arguments

a model to be explained, or an explainer created with function `DALEX::explain()`.

...

parameters passed to `local_*` functions.

interactions

shall interactions be included?

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's an explainer.

keep_distributions

if `TRUE`, then distribution of partial predictions is stored and can be plotted with the generic `plot()`.

order

if not `NULL`, then it will be a fixed order of variables. It can be a numeric vector or vector with names of variables.

label

name of the model. By default it is extracted from the 'class' attribute of the model.

Value

an object of the `break_down` class.

References

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

Examples

Run this code

# 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(explain_titanic_glm, titanic_small[1, ])
bd_rf
plot(bd_rf, max_features = 3)

# }
# NOT RUN {
## Not run:
library("randomForest")
set.seed(1313)
# example with interaction
# classification for HR data
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(explainer_rf,
                           new_observation)
bd_rf
plot(bd_rf)
# }