setup_HardCL

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

setup_HardCL function

<dl><dt>k</dt>
<dd>Integer [1, Inf): Number of clusters.</dd>
<dt>dist</dt>
<dd>Character {"euclidean", "manhattan"}: Distance measure to use.</dd>
<dt>features</dt>
<dd>Optional Character vector: Names of at least 2 distinct
feature columns to cluster on. <code>NULL</code> clusters on all numeric features.</dd></dl>

Arguments

Author

Setup HardCLConfig — setup_HardCL

<dl>

<dt>k</dt>
<dd>Integer [1, Inf): Number of clusters.</dd>


<dt>dist</dt>
<dd>Character {"euclidean", "manhattan"}: Distance measure to use.</dd>


<dt>features</dt>
<dd>Optional Character vector: Names of at least 2 distinct
feature columns to cluster on. <code>NULL</code> clusters on all numeric features.</dd>

</dl>

setup_HardCL: Setup HardCLConfig

Description

Usage

Value

Arguments

Author

Examples