Advanced Machine Learning with R

Random forest model

There are a number of approaches to learning in multiclass problems. Techniques such as random forest and discriminant analysis will deal with multiclass while some techniques and/or packages won't—for example, generalized linear models, glm(), in base R. The functionality built into mlr allows you to run a number of techniques for supervised and unsupervised learning. However, leveraging its power the first couple of times you use it can be a little confusing. If you follow the process outlined in the following, you'll be well on your way to developing powerful learning pipelines. We'll be using random forest in this demonstration. 

We've created the training and testing sets, which you can do in mlr, but I still prefer ...