9  Supervised Learning

All supervised learning in rtemis is done using the train() function.
train() can perform:

• Regression & Classification tasks are detected automatically, depending on the outcome variable’s type (numeric => Regression, factor => Classification)
• Automatic hyperparameter tuning: If more than one value is passed for a tunable hyperparameter, train() will automatically perform hyperparameter tuning, a.k.a. model selection.
• Training-test set splitting: Argument outer_resampling defines the resampling used for assessmenet of model performance.

9.1 Overview

Figure 9.1: Overview of the train() function

9.2 Conventions

• The input dataset columns should be either numeric or factors.
• The last column of the input data is the outcome variable.
• If the outcome variable is numeric, regression is performed.
• If the outcome variable is a factor, classification is performed.
• In binary classification, the second factor level is considered the positive case.