Author: Przemyslaw Biecek
Date: 2018-06-11

# model agnostic breakDown plots for ranger

#### Przemyslaw Biecek

#### 2018-06-11

Here we will use the HR churn data (https://www.kaggle.com/) to present the `breakDown` package for `ranger` models.

The data is in the `breakDown` package

    library(breakDown)
    head(HR_data, 3)
    #>   satisfaction_level last_evaluation number_project average_montly_hours
    #> 1               0.38            0.53              2                  157
    #> 2               0.80            0.86              5                  262
    #> 3               0.11            0.88              7                  272
    #>   time_spend_company Work_accident left promotion_last_5years sales salary
    #> 1                  3             0    1                     0 sales    low
    #> 2                  6             0    1                     0 sales medium
    #> 3                  4             0    1                     0 sales medium

Now let’s create a `ranger` classification forest for churn, the `left` variable.

    library(ranger)
    #>
    #> Attaching package: 'ranger'
    #> The following object is masked from 'package:randomForest':
    #>
    #>     importance
    HR_data$left <- factor(HR_data$left)
    model <- ranger(left ~ ., data = HR_data, importance = 'impurity', probability=TRUE, min.node.size = 2000)

    predict.function <- function(model, new_observation) predict(model, new_observation, type = "response")$predictions[,2]

    predict.function(model, HR_data[11,])
    #>         1
    #> 0.8667675

But how to understand which factors drive predictions for a single observation?

With the `breakDown` package!

Explanations for the trees votings.

    library(ggplot2)

    explain_1 <- broken(model, HR_data[11,-7], data = HR_data[,-7],
                        predict.function = predict.function,
                        direction = "down")
    explain_1
    #>                              contribution
    #> (Intercept)                         0.238
    #> - satisfaction_level = 0.45         0.061
    #> - number_project = 2                0.252
    #> - last_evaluation = 0.54            0.119
    #> - average_montly_hours = 135        0.108
    #> - time_spend_company = 3            0.075
    #> - Work_accident = 0                 0.011
    #> - salary = low                      0.002
    #> - promotion_last_5years = 0         0.000
    #> - sales = sales                     0.000
    #> final_prognosis                     0.867
    #> baseline:  0
    plot(explain_1) + ggtitle("breakDown plot  (direction=down) for ranger model")

    explain_2 <- broken(model, HR_data[11,-7], data = HR_data[,-7],
                        predict.function = predict.function,
                        direction = "up")
    explain_2
    #>                              contribution
    #> (Intercept)                         0.238
    #> + satisfaction_level = 0.45         0.061
    #> + number_project = 2                0.252
    #> + last_evaluation = 0.54            0.119
    #> + average_montly_hours = 135        0.108
    #> + time_spend_company = 3            0.075
    #> + Work_accident = 0                 0.011
    #> + salary = low                      0.002
    #> + promotion_last_5years = 0         0.000
    #> + sales = sales                     0.000
    #> final_prognosis                     0.867
    #> baseline:  0
    plot(explain_2) + ggtitle("breakDown plot (direction=up) for ranger model")