local_attributions function

Model Agnostic Sequential Variable attributions

This function finds Variable attributions via Sequential Variable Conditioning. The complexity of this function is O(2*p). This function works in a similar way to step-up and step-down greedy approximations in function break_down. The main difference is that in the first step the order of variables is determined. And in the second step the impact is calculated.

local_attributions(x, ...)

## S3 method for class 'explainer'
local_attributions(x, new_observation, keep_distributions = FALSE, ...)

## Default S3 method:
local_attributions(
  x,
  data,
  predict_function = predict,
  new_observation,
  label = class(x)[1],
  keep_distributions = FALSE,
  order = NULL,
  ...
)

Arguments

• x: an explainer created with function explain or a model.
• ...: other parameters.
• new_observation: a new observation with columns that correspond to variables used in the model.
• keep_distributions: if TRUE, then distribution of partial predictions is stored and can be plotted with the generic plot().
• 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.
• 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.

Returns

an object of the break_down class.

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

bd_rf <- local_attributions(explainer_rf,
                           new_observation)
bd_rf
plot(bd_rf)
plot(bd_rf, baseline = 0)

# example for regression - apartment prices
# here we do not have interactions
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 <- local_attributions(explainer_rf,
                           apartments_test[1,])
bd_rf
plot(bd_rf, digits = 1)

bd_rf <- local_attributions(explainer_rf,
                           apartments_test[1,],
                           keep_distributions = TRUE)
plot(bd_rf, plot_distributions = TRUE)
## End(Not run)

References

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

See Also

break_down, local_interactions