# General introduction: iBreakDown plots for Sinking of the RMS Titanic

#### Przemyslaw Biecek

#### 2020-07-28

# 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")
    head(titanic)

    #>   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: 0.1427817
    #>                     % Var explained: 34.86

# 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")

    #> 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  :  numerical, min =  0.008105044 , mean =  0.324085 , max =  0.9925386
    #>   -> model_info        :  package randomForest , ver. 4.6.14 , task regression ( [33m default [39m )
    #>   -> residual function :  difference between y and yhat ( [33m default [39m )
    #>   -> residuals         :  numerical, min =  -0.7889811 , mean =  0.0003552274 , max =  0.9103869
    #>  [32m A new explainer has been created! [39m

# 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

    library("iBreakDown")
    rf_la <- local_attributions(explain_titanic_rf, new_passanger)
    rf_la

    #>                                          contribution
    #> Random Forest v7: intercept                     0.324
    #> Random Forest v7: age = 8                       0.211
    #> Random Forest v7: class = 1st                   0.073
    #> Random Forest v7: gender = male                -0.052
    #> Random Forest v7: embarked = Southampton       -0.011
    #> Random Forest v7: sibsp = 0                    -0.001
    #> Random Forest v7: fare = 72                    -0.055
    #> Random Forest v7: parch = 0                    -0.024
    #> Random Forest v7: prediction                    0.465

## Plot attributions with `ggplot2`

    plot(rf_la)

## Plot attributions with `D3`

    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)