Business and data understanding

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...