Author: Przemyslaw Biecek
Date: 2018-06-11

# model agnostic breakDown plots for randomForest

#### Przemyslaw Biecek

#### 2018-06-11

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

The data is in the `breakDown` package

    set.seed(1313)

    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 logistic regression model for churn, the `left` variable.

    library("randomForest")
    #> randomForest 4.6-14
    #> Type rfNews() to see new features/changes/bug fixes.
    #>
    #> Attaching package: 'randomForest'
    #> The following object is masked from 'package:ggplot2':
    #>
    #>     margin
    model <- randomForest(factor(left)~., data = HR_data, family = "binomial", maxnodes = 5)

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

With the `breakDown` package!

Explanations for the linear predictor.

    library(ggplot2)

    predict.function <- function(model, new_observation) predict(model, new_observation, type="prob")[,2]
    predict.function(model, HR_data[11,-7])
    #> [1] 0.888

    explain_1 <- broken(model, HR_data[11,-7], data = HR_data[,-7],
                        predict.function = predict.function,
                        direction = "down")
    explain_1
    #>                              contribution
    #> (Intercept)                         0.148
    #> - satisfaction_level = 0.45         0.133
    #> - number_project = 2                0.201
    #> - last_evaluation = 0.54            0.182
    #> - average_montly_hours = 135        0.141
    #> - time_spend_company = 3            0.068
    #> - Work_accident = 0                 0.010
    #> - salary = low                      0.005
    #> - sales = sales                     0.000
    #> - promotion_last_5years = 0         0.000
    #> final_prognosis                     0.888
    #> baseline:  0
    plot(explain_1) + ggtitle("breakDown plot  (direction=down) for randomForest model")

    explain_2 <- broken(model, HR_data[11,-7], data = HR_data[,-7],
                        predict.function = predict.function,
                        direction = "up")
    explain_2
    #>                              contribution
    #> (Intercept)                         0.148
    #> + satisfaction_level = 0.45         0.133
    #> + number_project = 2                0.201
    #> + last_evaluation = 0.54            0.182
    #> + average_montly_hours = 135        0.141
    #> + time_spend_company = 3            0.068
    #> + Work_accident = 0                 0.010
    #> + salary = low                      0.005
    #> + promotion_last_5years = 0         0.000
    #> + sales = sales                     0.000
    #> final_prognosis                     0.888
    #> baseline:  0
    plot(explain_2) + ggtitle("breakDown plot (direction=up) for randomForest model")