Model Agnostic Sequential Variable Attributions

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

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 = FALSE
)

Arguments

x

an explainer created with function explain or a model.

...

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

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

See also

local_attributions, local_interactions

Examples

library("DALEX")
#> Welcome to DALEX (version: 2.3.0).
#> Find examples and detailed introduction at: http://ema.drwhy.ai/
#> Additional features will be available after installation of: ggpubr.
#> Use 'install_dependencies()' to get all suggested dependencies
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")
#> Preparation of a new explainer is initiated
#>   -> model label       :  glm 
#>   -> data              :  2207  rows  8  cols 
#>   -> target variable   :  2207  values 
#>   -> predict function  :  yhat.glm  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package stats , ver. 4.1.2 , task classification (  default  ) 
#>   -> predicted values  :  numerical, min =  0.1490412 , mean =  0.3221568 , max =  0.9878987  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -0.8898433 , mean =  4.198546e-13 , max =  0.8448637  
#>   A new explainer has been created!  

bd_glm <- break_down(explain_titanic_glm, titanic_imputed[1, ])
bd_glm
#>                             contribution
#> glm: intercept                     0.322
#> glm: gender = male                -0.107
#> glm: fare = 7.11                  -0.018
#> glm: age = 42                     -0.014
#> glm: class = 3rd                   0.000
#> glm: embarked = Southampton        0.000
#> glm: sibsp = 0                     0.000
#> glm: parch = 0                     0.000
#> glm: survived = 0                  0.000
#> glm: prediction                    0.183
plot(bd_glm, max_features = 3)


# \dontrun{
## Not run:
library("randomForest")
#> randomForest 4.7-1
#> Type rfNews() to see new features/changes/bug fixes.
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])
#> Preparation of a new explainer is initiated
#>   -> model label       :  randomForest  (  default  )
#>   -> data              :  1000  rows  5  cols 
#>   -> target variable   :  not specified! (  WARNING  )
#>   -> predict function  :  yhat.randomForest  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package randomForest , ver. 4.7.1 , task multiclass (  default  ) 
#>   -> model_info        :  Model info detected multiclass task but 'y' is a NULL .  (  WARNING  )
#>   -> model_info        :  By deafult multiclass tasks supports only factor 'y' parameter. 
#>   -> model_info        :  Consider changing to a factor vector with true class names.
#>   -> model_info        :  Otherwise I will not be able to calculate residuals or loss function.
#>   -> predicted values  :  predict function returns multiple columns:  3  (  default  ) 
#>   -> residual function :  difference between 1 and probability of true class (  default  )
#>   A new explainer has been created!  

bd_rf <- break_down(explainer_rf,
                           new_observation)
head(bd_rf)
#>                                    contribution
#> randomForest.fired: intercept             0.386
#> randomForest.fired: hours = 42.32         0.231
#> randomForest.fired: evaluation = 2        0.062
#> randomForest.fired: salary = 2           -0.272
#> randomForest.fired: age = 57.73           0.092
#> randomForest.fired: gender = male         0.281
plot(bd_rf)

# }