We will begin by creating our training and testing sets, then create a random forest classifier as our base model. After evaluating its performance, we will move on and try the one-versus-rest classification method and see how it performs. We split our data 70/30. Also, one of the unique things about the mlr package is its requirement to put your training data into a "task" structure, specifically a classification task. Optionally, you can place your test set in a task as well.
A full list of models is available here, plus you can also utilize your own:
https://mlr-org.github.io/mlr-tutorial/release/html/integrated_learners/index.html
> library(caret) #if not already loaded
> set.seed(502)
> split <- createDataPartition(y = df$class, p = 0.7, list = F)
> train <- df[split, ]
> test <- df[-split, ]
> wine.task <- makeClassifTask...
