We are once again going to visit our wine data set that we used in Chapter 8, Cluster Analysis. If you recall, it consists of 13 numeric features and a response of three possible classes of wine. Our task is to predict those classes. I will include one interesting twist and that is to artificially increase the number of observations. The reasons are twofold. First, I want to fully demonstrate the resampling capabilities of the mlr package, and second, I wish to cover a synthetic sampling technique. We utilized upsampling in the prior section, so synthetic is in order.

Our first task is to load the package libraries and bring the data:

    > library(mlr)

    > library(ggplot2)

    > library(HDclassif)

    > library(DMwR)

    > library(reshape2)

    > library(corrplot)

    > data(wine)

    > table(wine$class)

     1  2  3 
    59 71 48

We have 178 observations, plus the response labels are numeric (1, 2 and 3). Let's more than double...