setup_NNLS

Setup hyperparameters for non-negative least squares.

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_NNLS function

<dl><dt>normalize</dt>
<dd>(Tunable) Logical: If TRUE, scale the coefficients to sum to
1.</dd>
<dt>ifw</dt>
<dd>(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in
classification.</dd></dl>

Arguments

Author

Setup NNLS Hyperparameters — setup_NNLS

<dl>

<dt>normalize</dt>
<dd>(Tunable) Logical: If TRUE, scale the coefficients to sum to
1.</dd>


<dt>ifw</dt>
<dd>(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in
classification.</dd>

</dl>

setup_NNLS: Setup NNLS Hyperparameters

Description

Usage

Value

Arguments

Author

Details

Examples