General introduction: iBreakDown plots for Sinking of the RMS Titanic

  collapse = FALSE,
  comment = "#>",
  fig.width = 7,
  fig.height = 3.5,
  warning = FALSE,
  message = FALSE

Data for Titanic survival

Let's see an example for iBreakDown plots for survival probability of Titanic passengers. First, let's see the data, we will find quite nice data from in the DALEX package (orginally stablelearner).


Model for Titanic survival

Ok, now it's time to create a model. Let's use the Random Forest model.

# prepare model
titanic <- na.omit(titanic)
model_titanic_rf <- randomForest(survived == "yes" ~ gender + age + class + embarked +
                                   fare + sibsp + parch,  data = titanic)

Explainer for Titanic survival

The third step (it's optional but useful) is to create a DALEX explainer for Random Forest model.

explain_titanic_rf <- explain(model_titanic_rf, 
                      data = titanic[,-9],
                      y = titanic$survived == "yes", 
                      label = "Random Forest v7")

Break Down plot with D3

Let's see Break Down for model predictions for 8 years old male from 1st class that embarked from port C.

new_passanger <- data.frame(
  class = factor("1st", levels = c("1st", "2nd", "3rd", "deck crew", "engineering crew", "restaurant staff", "victualling crew")),
  gender = factor("male", levels = c("female", "male")),
  age = 8,
  sibsp = 0,
  parch = 0,
  fare = 72,
  embarked = factor("Southampton", levels = c("Belfast", "Cherbourg", "Queenstown", "Southampton"))

Calculate variable attributions

rf_la <- local_attributions(explain_titanic_rf, new_passanger)

Plot attributions with ggplot2


Plot attributions with D3


Calculate uncertainty for variable attributions

rf_la_un <- break_down_uncertainty(explain_titanic_rf, new_passanger,
                         path = "average")

Show only top features

plotD3(rf_la, max_features = 3)

Force OX axis to be from 0 to 1

plotD3(rf_la, max_features = 3, min_max = c(0,1), margin = 0)

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iBreakDown documentation built on May 7, 2021, 5:07 p.m.