setup_Bootstrap

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_Bootstrap function

<dl><dt>n_resamples</dt>
<dd>Integer [1, Inf): Number of resamples to make.</dd>
<dt>id_strat</dt>
<dd>Optional Character: Name of the column holding per-case grouping IDs, e.g.
subject IDs when the dataset contains repeated measurements. Cases sharing an ID stay in the
same resample, so a case can only be present in the training or the test set, not both.
Requires that <code>resample()</code> be given the data frame the column lives in. train takes the
column as a case identifier, so it groups the resamples by it and excludes it from the
features.</dd>
<dt>seed</dt>
<dd>Optional Integer [0, Inf): Random seed.</dd></dl>

Arguments

Author

Setup Bootstrap Resampler — setup_Bootstrap

<dl>

<dt>n_resamples</dt>
<dd>Integer [1, Inf): Number of resamples to make.</dd>


<dt>id_strat</dt>
<dd>Optional Character: Name of the column holding per-case grouping IDs, e.g.
subject IDs when the dataset contains repeated measurements. Cases sharing an ID stay in the
same resample, so a case can only be present in the training or the test set, not both.
Requires that <code>resample()</code> be given the data frame the column lives in. train takes the
column as a case identifier, so it groups the resamples by it and excludes it from the
features.</dd>


<dt>seed</dt>
<dd>Optional Integer [0, Inf): Random seed.</dd>

</dl>

setup_Bootstrap: Setup Bootstrap Resampler

Description

Usage

Value

Arguments

Author

Examples