rtemis ML

rtemis Machine Learning

E.D. Gennatas, MBBS AICSM PhD

rtemis provides a common R interface for supervised learning, dimensionality reduction, and clustering. Typed S7 configurations describe the analysis; fitted objects retain predictions, metrics, configuration, and run information.

This guide was built with rtemis version 1.4.2 and R version 4.6.1 (2026-06-24).

Reading the guide

The chapters follow the stages of an analysis:

1. Install the package and run a first model.
2. Read data, check its structure, and define partitions and resamples.
3. Specify preprocessing, model training, tuning, and execution.
4. Evaluate regression or classification, then examine calibration, prediction regions, and feature contributions.
5. Save configurations and results and produce model reports.

The method chapters cover tree ensembles, stacking, linear additive trees, decomposition, clustering, and mass-univariate analysis. Each executable chapter uses built-in or generated data. Examples that require external services or additional runtime setup are identified in the text.

Function arguments and class properties are documented in the API reference.