Author: Przemyslaw Biecek
Date: 2024-03-11

# How to use breakDown package for models created with caret

#### Przemyslaw Biecek

#### 2024-03-11

This example demonstrates how to use the `breakDown` package for models created with the caret package.

First we will generate some data.

    library(caret)

    set.seed(2)
    training <- twoClassSim(50, linearVars = 2)
    trainX <- training[, -ncol(training)]
    trainY <- training$Class

    head(training)
    #>   TwoFactor1 TwoFactor2    Linear1    Linear2 Nonlinear1 Nonlinear2 Nonlinear3
    #> 1 -0.6561702 -1.6480450  1.0744594  0.9758906  0.2342843  0.6805653  0.6920055
    #> 2 -0.9849973  1.4598834  0.2605978 -0.1694232  0.1381283  0.7460168  0.5599569
    #> 3  2.3722541  1.7069944 -0.3142720  0.7221918 -0.6920591  0.4642024  0.3426912
    #> 4 -2.2067173 -0.6972704 -0.7496301 -0.8444186 -0.9303336  0.1374181  0.2344975
    #> 5  0.5166671 -0.7228376 -0.8621983  1.2772937  0.9959069  0.8143796  0.4296028
    #> 6  1.3331262 -0.9929323  2.0480403 -1.3431105  0.6711474  0.8321613  0.7367007
    #>    Class
    #> 1 Class1
    #> 2 Class2
    #> 3 Class1
    #> 4 Class2
    #> 5 Class1
    #> 6 Class1

Now we are ready to train a model. Let’s train a `glm` model with `caret`.

    cctrl1 <- trainControl(method = "cv", number = 3, returnResamp = "all",
                           classProbs = TRUE,
                           summaryFunction = twoClassSummary)

    test_class_cv_model <- train(trainX, trainY,
                                 method = "glm",
                                 trControl = cctrl1,
                                 metric = "ROC",
                                 preProc = c("center", "scale"))
    test_class_cv_model
    #> Generalized Linear Model
    #>
    #> 50 samples
    #>  7 predictor
    #>  2 classes: 'Class1', 'Class2'
    #>
    #> Pre-processing: centered (7), scaled (7)
    #> Resampling: Cross-Validated (3 fold)
    #> Summary of sample sizes: 33, 34, 33
    #> Resampling results:
    #>
    #>   ROC        Sens       Spec
    #>   0.7771991  0.7175926  0.8009259

To use `breakDown` we need a function that will calculate scores/predictions for a single observation. By default the `predict()` function returns predicted class.

So we are adding `type = "prob"` argument to get scores. And since there will be two scores for each observarion we need to extract one of them.

    predict.fun <- function(model, x) predict(model, x, type = "prob")[,1]
    testing <- twoClassSim(10, linearVars = 2)
    predict.fun(test_class_cv_model, testing[1,])
    #> [1] 0.9807632

Now we are ready to call the `broken()` function.

    library("breakDown")
    explain_2 <- broken(test_class_cv_model, testing[1,], data = trainX, predict.function = predict.fun)
    explain_2
    #>                                   contribution
    #> (Intercept)                              0.500
    #> + TwoFactor2 = -2.15297519239414         0.330
    #> + Linear2 = 1.21347759171666             0.103
    #> + Nonlinear2 = 0.938861106755212         0.037
    #> + Nonlinear3 = 0.198311409447342         0.016
    #> + Linear1 = -1.59104698624311            0.006
    #> + Nonlinear1 = -0.693807001691312       -0.001
    #> + TwoFactor1 = -1.5957842151878         -0.009
    #> final_prognosis                          0.981
    #> baseline:  0

And plot it.

    library(ggplot2)
    plot(explain_2) + ggtitle("breakDown plot for caret/glm model")