9  Supervised Learning

  .:rtemis 1.3.5 🌊 aarch64-apple-darwin23

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_config defines the resampling used for assessment 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.

9.3 Available Algorithms

Print available algorithms:

available_supervised()

                     BART: Bayesian Additive Regression Trees
                     CART: Classification and Regression Trees
  ConditionalSuperLearner: Conditional SuperLearner
                      GAM: Generalized Additive Model
                      GLM: Generalized Linear Model
                   GLMNET: Elastic Net
                      HAL: Highly Adaptive Lasso
                 Isotonic: Isotonic Regression
             MonotonicHAL: Monotonic Highly Adaptive Lasso
                      KNN: k-Nearest Neighbors
                LightCART: Decision Tree
                 LightGBM: Gradient Boosting
                  LightRF: LightGBM Random Forest
             LightRuleFit: LightGBM RuleFit
                     MARS: Multivariate Adaptive Regression Splines
                      MLP: Multilayer Perceptron
         ModalityStacking: Per-Modality Stacked Ensemble
                     NNLS: Non-negative Least Squares
                   Ranger: Random Forest
             SuperLearner: Cross-validated Stacked Ensemble
                LinearSVM: Support Vector Machine with Linear Kernel
                RadialSVM: Support Vector Machine with Radial Kernel
                     SPLS: Sparse Partial Least Squares
                   TabNet: Attentive Interpretable Tabular Learning