setup_MultiASWCriterion

Setup the subsampled average silhouette width criterion for setup_PAMK:
the silhouette width is computed on random subsets of the cases and
averaged, which makes the score affordable on many cases.

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_MultiASWCriterion function

<dl><dt>n_subsets</dt>
<dd>Optional Integer [1, Inf): Number of random subsets the
average silhouette width is computed on.</dd></dl>

Arguments

Author

Setup MultiASWCriterionConfig — setup_MultiASWCriterion

<dl>

<dt>n_subsets</dt>
<dd>Optional Integer [1, Inf): Number of random subsets the
average silhouette width is computed on.</dd>

</dl>

setup_MultiASWCriterion: Setup MultiASWCriterionConfig

Description

Usage

Value

Arguments

Author

Details

Examples