setup_RadialSVM

Setup hyperparameters for RadialSVM training.

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_RadialSVM function

<dl><dt>cost</dt>
<dd>(Tunable) Numeric (0, Inf): Cost of constraints violation.</dd>
<dt>gamma</dt>
<dd>(Tunable) Numeric (0, Inf): Kernel coefficient.</dd>
<dt>ifw</dt>
<dd>(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in classification.</dd></dl>

Arguments

Author

Setup RadialSVM Hyperparameters — setup_RadialSVM

<dl>

<dt>cost</dt>
<dd>(Tunable) Numeric (0, Inf): Cost of constraints violation.</dd>


<dt>gamma</dt>
<dd>(Tunable) Numeric (0, Inf): Kernel coefficient.</dd>


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

</dl>

setup_RadialSVM: Setup RadialSVM Hyperparameters

Description

Usage

Value

Arguments

Author

Details

Examples