get_tuning_params_for_complexity

Returns algorithm-specific tuning parameter ranges adjusted for the
specified complexity level.

Provides a guarded resampling workflow for training and evaluating machine-learning models.
When the guarded resampling path is used, preprocessing and model fitting are re-estimated within
each resampling split to reduce leakage risk. Supports multiple resampling schemes, integrates
with established engines in the 'tidymodels' ecosystem, and aims to improve evaluation reliability by
coordinating preprocessing, fitting, and evaluation within supported workflows. Offers a lightweight
AutoML-style workflow by automating model training, resampling, and tuning across multiple algorithms,
while keeping evaluation design explicit and user-controlled.

Selcuk Korkmaz

fastml

Guarded Resampling Workflows for Safe and Automated Machine
Learning in R

Dincer Goksuluk

Eda Karaismailoglu

get_tuning_params_for_complexity function

<dl><dt>algo</dt>
<dd>Character string specifying the algorithm name.</dd>
<dt>train_data</dt>
<dd>Data frame containing the training data.</dd>
<dt>label</dt>
<dd>Character string specifying the outcome variable name.</dd>
<dt>engine</dt>
<dd>Character string specifying the engine.</dd>
<dt>complexity</dt>
<dd>Character string specifying tuning complexity level.</dd></dl>

Arguments

Get Tuning Parameters for Complexity Level — get_tuning_params_for_complexity

<dl>

<dt>algo</dt>
<dd>Character string specifying the algorithm name.</dd>


<dt>train_data</dt>
<dd>Data frame containing the training data.</dd>


<dt>label</dt>
<dd>Character string specifying the outcome variable name.</dd>


<dt>engine</dt>
<dd>Character string specifying the engine.</dd>


<dt>complexity</dt>
<dd>Character string specifying tuning complexity level.</dd>

</dl>

get_tuning_params_for_complexity: Get Tuning Parameters for Complexity Level

Description

Usage

Value

Arguments

Details