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).
#> gender age class embarked country fare sibsp parch survived
#> 1 male 42 3rd Southampton United States 7.11 0 0 no
#> 2 male 13 3rd Southampton United States 20.05 0 2 no
#> 3 male 16 3rd Southampton United States 20.05 1 1 no
#> 4 female 39 3rd Southampton England 20.05 1 1 yes
#> 5 female 16 3rd Southampton Norway 7.13 0 0 yes
#> 6 male 25 3rd Southampton United States 7.13 0 0 yes
Ok, now it’s time to create a model. Let’s use the Random Forest model.
# prepare model
library("randomForest")
titanic <- na.omit(titanic)
model_titanic_rf <- randomForest(survived == "yes" ~ gender + age + class + embarked +
fare + sibsp + parch, data = titanic)
model_titanic_rf#>
#> Call:
#> randomForest(formula = survived == "yes" ~ gender + age + class + embarked + fare + sibsp + parch, data = titanic)
#> Type of random forest: regression
#> Number of trees: 500
#> No. of variables tried at each split: 2
#>
#> Mean of squared residuals: 0.1432021
#> % Var explained: 34.66
The third step (it’s optional but useful) is to create a DALEX explainer for Random Forest model.
library("DALEX")
explain_titanic_rf <- explain(model_titanic_rf,
data = titanic[,-9],
y = titanic$survived == "yes",
label = "Random Forest v7")#> Preparation of a new explainer is initiated
#> -> model label : Random Forest v7
#> -> 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 = 0.009671469 , mean = 0.3242068 , max = 0.9916235
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -0.7847014 , mean = 0.0002333619 , max = 0.8987924
#> A new explainer has been created!
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"))
)#> contribution
#> Random Forest v7: intercept 0.324
#> Random Forest v7: age = 8 0.206
#> Random Forest v7: class = 1st 0.070
#> Random Forest v7: gender = male -0.054
#> Random Forest v7: embarked = Southampton -0.013
#> Random Forest v7: sibsp = 0 0.001
#> Random Forest v7: fare = 72 -0.064
#> Random Forest v7: parch = 0 -0.026
#> Random Forest v7: prediction 0.443