get_expanded_tune_params

Returns expanded tuning parameter ranges that provide better coverage
than the minimal defaults. These are used when <code>tuning_complexity</code>
is set to "thorough" or "exhaustive".

internal

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_expanded_tune_params function

<dl><dt>algo</dt>
<dd>Algorithm name.</dd>
<dt>train_data</dt>
<dd>Training data frame.</dd>
<dt>label</dt>
<dd>Outcome variable name.</dd>
<dt>engine</dt>
<dd>Engine name.</dd></dl>

Arguments

Expanded Default Tuning Parameters — get_expanded_tune_params

<dl>

<dt>algo</dt>
<dd>Algorithm name.</dd>


<dt>train_data</dt>
<dd>Training data frame.</dd>


<dt>label</dt>
<dd>Outcome variable name.</dd>


<dt>engine</dt>
<dd>Engine name.</dd>

</dl>

get_expanded_tune_params: Expanded Default Tuning Parameters

Description

Usage

Value

Arguments