setup_Nystrom

Setup the Nystrom approximation for setup_SpectralRBF or
setup_SpectralLaplace. Passing one turns the approximation on; its
<code>sample</code> says how many cases it samples.

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_Nystrom function

<dl><dt>sample</dt>
<dd>Optional Integer [1, Inf): Number of cases the approximation
samples. Must be fewer than the number of cases. Unset samples one sixth of
them.</dd></dl>

Arguments

Author

Setup NystromConfig — setup_Nystrom

<dl>

<dt>sample</dt>
<dd>Optional Integer [1, Inf): Number of cases the approximation
samples. Must be fewer than the number of cases. Unset samples one sixth of
them.</dd>

</dl>

setup_Nystrom: Setup NystromConfig

Description

Usage

Value

Arguments

Author

Details

Examples