get_default_params_with_warnings

Wrapper around <code>get_default_params</code> that optionally warns when fastml's
default parameters differ from parsnip/engine defaults.

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_default_params_with_warnings function

<dl><dt>algo</dt>
<dd>Algorithm name.</dd>
<dt>task</dt>
<dd>Task type.</dd>
<dt>num_predictors</dt>
<dd>Number of predictors (optional).</dd>
<dt>engine</dt>
<dd>Engine name (optional).</dd>
<dt>warn_param_defaults</dt>
<dd>Logical; if TRUE, warn about parameter differences.</dd>
<dt>verbose</dt>
<dd>Logical; if TRUE, print parameter selections.</dd></dl>

Arguments

Get Default Parameters with Transparency Warnings — get_default_params_with_warnings

<dl>

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


<dt>task</dt>
<dd>Task type.</dd>


<dt>num_predictors</dt>
<dd>Number of predictors (optional).</dd>


<dt>engine</dt>
<dd>Engine name (optional).</dd>


<dt>warn_param_defaults</dt>
<dd>Logical; if TRUE, warn about parameter differences.</dd>


<dt>verbose</dt>
<dd>Logical; if TRUE, print parameter selections.</dd>

</dl>

get_default_params_with_warnings: Get Default Parameters with Transparency Warnings

Description

Usage

Value

Arguments