# 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`).

``````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 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: 0.1423729
#>                     % Var explained: 35.04``````

# Explainer for Titanic survival

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")``````

# 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"))
)``````

``````library("iBreakDown")
rf_la``````
``````#>                                          contribution
#> Random Forest v7: intercept                     0.325
#> Random Forest v7: age = 8                       0.198
#> Random Forest v7: class = 1st                   0.074
#> Random Forest v7: gender = male                -0.052
#> Random Forest v7: fare = 72                    -0.070
#> Random Forest v7: embarked = Southampton       -0.026
#> Random Forest v7: sibsp = 0                    -0.004
#> Random Forest v7: parch = 0                    -0.029
#> Random Forest v7: prediction                    0.415``````

## Plot attributions with `ggplot2`

``plot(rf_la)``