# Data for Titanic survival

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

library("DALEX")
#>   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

# Model for Titanic survival

Ok, now itâ€™s time to create a model. Letâ€™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: 0.1427573
#>                     % Var explained: 34.87

# Explainer for Titanic survival

The third step (itâ€™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 ( [33m default [39m )
#>   -> predicted values  :  No value for predict function target column. ( [33m default [39m )
#>   -> model_info        :  package randomForest , ver. 4.6.14 , task regression ( [33m default [39m )
#>   -> model_info        :  Model info detected regression task but 'y' is a logical .  ( [31m WARNING [39m )
#>   -> 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 =  0.0110828 , mean =  0.3237894 , max =  0.9905016
#>   -> residual function :  difference between y and yhat ( [33m default [39m )
#>   -> residuals         :  numerical, min =  -0.8039163 , mean =  0.0006508012 , max =  0.9098307
#>  [32m A new explainer has been created! [39m

# Break Down plot with D3

Letâ€™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"))
)

library("iBreakDown")
rf_la
#>                                          contribution
#> Random Forest v7: intercept                     0.324
#> Random Forest v7: age = 8                       0.210
#> Random Forest v7: class = 1st                   0.061
#> Random Forest v7: gender = male                -0.051
#> Random Forest v7: fare = 72                    -0.063
#> Random Forest v7: embarked = Southampton       -0.018
#> Random Forest v7: sibsp = 0                    -0.004
#> Random Forest v7: parch = 0                    -0.028
#> Random Forest v7: prediction                    0.430

plot(rf_la)

plotD3(rf_la)

## Calculate uncertainty for variable attributions

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

## 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)