available_supervised

available_algorithms

available_clustering

available_calibration

available_decomposition

Print available algorithms for supervised learning, clustering, and decomposition.

Machine learning and visualization package with an 'S7' backend featuring
comprehensive type checking and validation, observable runs, and auditable outputs.
The functional API provides one-call access to supervised and unsupervised learning
through train(), cluster(), and decomp(). All configuration steps are performed using
setup functions and validated. A single call to train() can handle preprocessing,
decomposition, hyperparameter tuning, model stacking, and testing with nested resampling.
Supports 'data.frame', 'data.table', and 'tibble' inputs and parallel execution. Plot
methods for trained and resampled supervised models draw interactive figures through
'rtemis.draw'. Includes native implementations of SuperLearner, RuleFit, and Linear
Additive Trees, and supports conformal prediction. The package first appeared in
E.D. Gennatas (2017)
<https://repository.upenn.edu/entities/publication/d81892ea-3087-4b71-a6f5-739c58626d64>.

Efstathios D. Gennatas

rtemis

Schema-Driven Machine Learning and Visualization

E.D. Gennatas

available_supervised function

<dl><dt>verbosity</dt>
<dd>Integer: Verbosity level.</dd>
<dt>traits</dt>
<dd>Logical: If TRUE, <code>available_decomposition()</code> prints and
returns the full trait table from decomposition_traits instead of the
named list of descriptions.</dd></dl>

Arguments

Author

Available Algorithms — available_supervised

<dl>

<dt>verbosity</dt>
<dd>Integer: Verbosity level.</dd>


<dt>traits</dt>
<dd>Logical: If TRUE, <code>available_decomposition()</code> prints and
returns the full trait table from decomposition_traits instead of the
named list of descriptions.</dd>

</dl>

available_supervised: Available Algorithms

Description

Usage

Value

Arguments

Author

Details

Examples