.:rtemis 0.99.98 🌊 aarch64-apple-darwin20
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
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.