partition

Splits <code>dat</code> by the rule in a <code>PartitionConfig</code> and returns a manifest of
the split, including a <code>DataFingerprint</code> of the source and of each
partition. With <code>outdir</code>, writes <code>training.parquet</code> and <code>test.parquet</code>,
which a <code>SuperConfig</code> references through <code>dat_training_path</code> and
<code>dat_test_path</code>.

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

partition function

<dl><dt>dat</dt>
<dd>data.frame or data.table: The dataset to split.</dd>
<dt>config</dt>
<dd><code>PartitionConfig</code>: The split rule.</dd>
<dt>outdir</dt>
<dd>Optional Character: Directory to write <code>training.parquet</code> and
<code>test.parquet</code> to; created if it does not exist. If <code>NULL</code>, nothing is
written and each <code>outputs</code> entry carries its fingerprint and row count
without a <code>path</code>.</dd>
<dt>overwrite</dt>
<dd>Logical: If TRUE, overwrite existing output files.</dd>
<dt>verbosity</dt>
<dd>Integer: Verbosity level.</dd></dl>

Arguments

Author

Split a dataset into training and held-out test partitions — partition

<dl>

<dt>dat</dt>
<dd>data.frame or data.table: The dataset to split.</dd>


<dt>config</dt>
<dd><code>PartitionConfig</code>: The split rule.</dd>


<dt>outdir</dt>
<dd>Optional Character: Directory to write <code>training.parquet</code> and
<code>test.parquet</code> to; created if it does not exist. If <code>NULL</code>, nothing is
written and each <code>outputs</code> entry carries its fingerprint and row count
without a <code>path</code>.</dd>


<dt>overwrite</dt>
<dd>Logical: If TRUE, overwrite existing output files.</dd>


<dt>verbosity</dt>
<dd>Integer: Verbosity level.</dd>

</dl>

partition: Split a dataset into training and held-out test partitions

Description

Usage

Value

Arguments

Author

Examples