.list_to_SuperConfig

Reconstructs a <code>SuperConfig</code> from a named list, such as the result of
parsing a JSON config; read_config uses it for supervised documents. Nested
config objects are rebuilt via their respective <code>.list_to_*</code> / <code>setup_*</code>
functions.

internal

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

.list_to_SuperConfig function

<dl><dt>x</dt>
<dd>Named list carrying <code>SuperConfig</code> fields (e.g. <code>hyperparameters</code>,
<code>decomposition_config</code>, <code>outer_resampling_config</code>).</dd></dl>

Arguments

Author

Convert a list to a SuperConfig object — .list_to_SuperConfig

<dl>

<dt>x</dt>
<dd>Named list carrying <code>SuperConfig</code> fields (e.g. <code>hyperparameters</code>,
<code>decomposition_config</code>, <code>outer_resampling_config</code>).</dd>

</dl>

.list_to_SuperConfig: Convert a list to a `SuperConfig` object

Description

Usage

Value

Arguments

Author

Details

Examples