iBreakDown Package
The iBreakDown package allows for generating feature
attribution explanations. Feature attribution explanations justify a
model’s prediction by showing which of the model’s variables affect the
prediction and to what extent. It is done by attaching to each variable
an importance coefficient, which sum should approximate the model’s
prediction.
There are two methods used by iBreakDown package:
shap() and break_down().
Function shap() generates a SHAP explanation, that is,
the function assigns Shapley values to each variable. Function
break_down uses break_down algorithm to generate an
efficient approximation of the Shapley values. We show how to generate
both explanations on an easy example using titanic data set and
explainers from DALEX package.
First, we load the data set and build a random forest model
classifying which of the passengers survived the sinking of the Titanic.
Then, using DALEX package, we generate an explainer of the
model. Lastly, we select a random passenger, which prediction’s should
be explained.
library("DALEX")
library("iBreakDown")
library("randomForest")
titanic <- na.omit(titanic)
model_titanic_rf <- randomForest(survived == "yes" ~ .,
data = titanic
)
explain_titanic_rf <- explain(model_titanic_rf,
data = titanic[,-9],
y = titanic$survived == "yes",
label = "Random Forest")
#> Preparation of a new explainer is initiated
#> -> model label : Random Forest
#> -> data : 2099 rows 8 cols
#> -> target variable : 2099 values
#> -> predict function : yhat.randomForest will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package randomForest , ver. 4.7.1.1 , task regression ( default )
#> -> model_info : Model info detected regression task but 'y' is a logical . ( WARNING )
#> -> model_info : By deafult regressions tasks supports only numercical 'y' parameter.
#> -> model_info : Consider changing to numerical vector.
#> -> model_info : Otherwise I will not be able to calculate residuals or loss function.
#> -> predicted values : numerical, min = 0.01016263 , mean = 0.3231976 , max = 0.9912657
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -0.7981174 , mean = 0.00124263 , max = 0.8988771
#> A new explainer has been created!
passanger <- titanic[sample(nrow(titanic), 1) ,-9]
passanger
#> gender age class embarked country fare sibsp parch
#> 2107 male 35 victualling crew Southampton England 0 0 0
Now we are ready for generating shap() and
iBreakDown() explanations.
bd_rf <- break_down(explain_titanic_rf,
passanger,
keep_distributions = TRUE) # distributions should be kept
shap_rf <- shap(explain_titanic_rf,
passanger)
plot(bd_rf)
Describing an explanation
The displayed explanations, despite their visual clarity, may not be
interpretable for someone not familiar with iBreakDown or shap
explanation. Therefore, we generate a simple natural language
description for both explainers.
#> Random Forest predicts, that the prediction for the selected instance is 0.151 which is lower than the average model prediction. For the selected instance model's prediction is lower, than for 70% of all observations.
#>
#> The most important variable that decrease the prediction is gender.
#>
#> Other variables are with less importance. The contribution of all other variables is -0.008.
#> Random Forest predicts, that the prediction for the selected instance is 0.151 which is lower than the average model prediction. For the selected instance model's prediction is lower, than for 70% of all observations.
#>
#> The most important variable that decrease the prediction is gender.
#>
#> Other variables are with less importance. The contribution of all other variables is -0.007.
Parameters of describe() function
Natural language descriptions should be flexible enough to generate a
description with the desired level of specificity and length. We
describe the parameters used for describing both explanations. As both
explanations have the same parameters, we turn our attention to describe
the iBreakDown explanation.
Adjusting nonsignificance treshold
The nonsignificance treshold controls which predictions are close to
the average prediction. By setting a higher value, more predictions will
be described as close to the average model prediction, and more
variables will be described as nonsignificant.
describe(bd_rf, nonsignificance_treshold = 1)
#> Random Forest predicts, that the prediction for the selected instance is 0.151 which is close to the average model prediction. For the selected instance model's prediction is lower, than for 70% of all observations.
#>
#> The most important variable that decrease the prediction is gender.
#>
#> Other variables are with less importance. The contribution of all other variables is -0.008.
Adjusting label of the explanation
The label of the prediction could be changed to display more specific
descriptions.
describe(bd_rf,
label = "the passanger survived with probability")
#> Random Forest predicts, that the passanger survived with probability 0.151 which is lower than the average model prediction. For the selected instance model's prediction is lower, than for 70% of all observations.
#>
#> The most important variable that decrease the prediction is gender.
#>
#> Other variables are with less importance. The contribution of all other variables is -0.008.
Short descriptions
Generating short descriptions can be useful, as they can make nice
plot subtitles.
describe(bd_rf, short_description = TRUE)
#> Random Forest predicts, that the prediction for the selected instance is 0.151. The most important variable, which decreases model's prediction, is gender.
Displaying values
Displaying variable values can easily make the description more
informative.
describe(bd_rf, display_values = TRUE)
#> Random Forest predicts, that the prediction for the selected instance is 0.151 which is lower than the average model prediction. For the selected instance model's prediction is lower, than for 70% of all observations.
#>
#> The most important variable that decrease the prediction is gender (= male).
#>
#> Other variables are with less importance. The contribution of all other variables is -0.008.
Displaying numbers
Displaying numbers changes the whole argumentation style making the
description longer.
describe(bd_rf, display_numbers = TRUE)
#> Random Forest predicts, that the prediction for the selected instance is 0.151 which is lower than the average model prediction 0.323. For the selected instance model's prediction is lower, than for 70% of all observations.
#>
#> The most important variable is gender. It decreases the prediction by 0.099.
#> The second most important variable is country. It decreases the prediction by 0.034.
#> The third most important variable is age. It decreases the prediction by 0.031.
#>
#> Other variables are with less importance. The contribution of all other variables is -0.008.
Distribution details
Describing distribution details is useful if we want to have a big
picture about other instances’ behaviour.
describe(bd_rf, display_distribution_details = TRUE)
#> Random Forest predicts, that the prediction for the selected instance is 0.151 which is lower than the average model prediction.Model predictions range from 0.01 to 0.991. The distribution of Random Forest's predictions is right-skewed with average equal to 0.323 and median equal to 0.211. The standard deviation is 0.28. Model's prediction for the selected instance is in the second quartile.
#>
#> The most important variable that decrease the prediction is gender.
#>
#> Other variables are with less importance. The contribution of all other variables is -0.008.
SHAP
Explanations generated by shap() functions have the same
arguments expected from display_shap, what add an
additional information, if the calculated variable’s contributions have
high or low variability.
describe(shap_rf, display_shap = TRUE)
#> Random Forest predicts, that the prediction for the selected instance is 0.151 which is lower than the average model prediction. For the selected instance model's prediction is lower, than for 70% of all observations.
#>
#> The most important variable that decrease the prediction is gender.
#> The average contribution of all the above variables is significant.
#>
#> Other variables are with less importance. The contribution of all other variables is -0.007.
Combining all the parameters
Of course, all the arguments can be set according to preferences
allowing for flexible natural language descriptions.
describe(shap_rf,
label = "the passanger survived with probability",
display_values = TRUE,
display_numbers = TRUE,
display_shap = TRUE)
#> Random Forest predicts, that the passanger survived with probability 0.151 which is lower than the average model prediction 0.323. For the selected instance model's prediction is lower, than for 70% of all observations.
#>
#> The most important variable is gender (= male). It decreases the prediction by 0.109.
#> The second most important variable is age (= 35). It decreases the prediction by 0.033.
#> The third most important variable is class (= victualling crew). It decreases the prediction by 0.023.
#> The average contribution of all the above variables is significant.
#>
#> Other variables are with less importance. The contribution of all other variables is -0.007.