9  Supervised Learning

  .:rtemis 0.99.98 🌊 aarch64-apple-darwin20

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

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.

9.3 Available Algorithms

Print available algorithms:

available_supervised()
          CART: Classification and Regression Trees
           GAM: Generalized Additive Model
           GLM: Generalized Linear Model
        GLMNET: Elastic Net
      Isotonic: Isotonic Regression
     LightCART: Decision Tree
      LightGBM: Gradient Boosting
       LightRF: LightGBM Random Forest
  LightRuleFit: LightGBM RuleFit
        Ranger: Ranger Random Forest
     LinearSVM: Support Vector Machine with Linear Kernel
     RadialSVM: Support Vector Machine with Radial Kernel
        TabNet: Attentive Interpretable Tabular Learning
Note

The new rtemis focuses on a a tighter integration with a smaller number of algorithms, at least initially. The original version supported a long list of algorithms that we ourselves never ended up using. Support for more algorithms, including our own, is planned and will be continuously reassessed.

© 2025 E.D. Gennatas