estimate_tuning_time

Provides a rough estimate of tuning time based on the configuration.

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

estimate_tuning_time function

<dl><dt>n_params</dt>
<dd>Number of parameters being tuned.</dd>
<dt>n_folds</dt>
<dd>Number of cross-validation folds.</dd>
<dt>n_rows</dt>
<dd>Number of rows in training data.</dd>
<dt>complexity</dt>
<dd>Tuning complexity level.</dd>
<dt>tuning_strategy</dt>
<dd>Tuning strategy ("grid" or "bayes").</dd>
<dt>base_fit_time</dt>
<dd>Estimated time for a single model fit in seconds.</dd></dl>

Arguments

Estimate Tuning Time — estimate_tuning_time

<dl>

<dt>n_params</dt>
<dd>Number of parameters being tuned.</dd>


<dt>n_folds</dt>
<dd>Number of cross-validation folds.</dd>


<dt>n_rows</dt>
<dd>Number of rows in training data.</dd>


<dt>complexity</dt>
<dd>Tuning complexity level.</dd>


<dt>tuning_strategy</dt>
<dd>Tuning strategy ("grid" or "bayes").</dd>


<dt>base_fit_time</dt>
<dd>Estimated time for a single model fit in seconds.</dd>

</dl>

estimate_tuning_time: Estimate Tuning Time

Description

Usage

Value

Arguments