Author: Przemyslaw Biecek
Date: 2018-06-11

# breakDown plots for the generalised linear models

#### 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

    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.

    model <- glm(left~., data = HR_data, family = "binomial")

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

With the `breakDown` package!

Explanations for the linear predictor.

    library(ggplot2)
    predict(model, HR_data[11,], type = "link")
    #>        11
    #> -0.262138

    explain_1 <- broken(model, HR_data[11,])
    explain_1
    #>                            contribution
    #> (Intercept)                      -1.601
    #> satisfaction_level = 0.45         0.673
    #> number_project = 2                0.568
    #> salary = low                      0.388
    #> average_montly_hours = 135       -0.295
    #> Work_accident = 0                 0.221
    #> time_spend_company = 3           -0.133
    #> last_evaluation = 0.54           -0.129
    #> promotion_last_5years = 0         0.030
    #> sales = sales                     0.014
    #> final_prognosis                  -0.262
    #> baseline:  0
    plot(explain_1) + ggtitle("breakDown plot for linear predictors")

Explanations for the probability with intercept set as an origin.

    predict(model, HR_data[11,], type = "response")
    #>        11
    #> 0.4348382

    explain_1 <- broken(model, HR_data[11,], baseline = "intercept")
    explain_1
    #>                            contribution
    #> (Intercept)                       0.000
    #> satisfaction_level = 0.45         0.673
    #> number_project = 2                0.568
    #> salary = low                      0.388
    #> average_montly_hours = 135       -0.295
    #> Work_accident = 0                 0.221
    #> time_spend_company = 3           -0.133
    #> last_evaluation = 0.54           -0.129
    #> promotion_last_5years = 0         0.030
    #> sales = sales                     0.014
    #> final_prognosis                   1.339
    #> baseline:  -1.601457
    plot(explain_1,
         trans = function(x) exp(x)/(1+exp(x))) + ggtitle("Predicted probability of leaving the company")+ scale_y_continuous( limits = c(0,1), name = "probability", expand = c(0,0))
    #> Scale for 'y' is already present. Adding another scale for 'y', which
    #> will replace the existing scale.