recommend_tuning_config

Provides recommendations for tuning configuration based on dataset
characteristics and time constraints.

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

recommend_tuning_config function

<dl><dt>n_rows</dt>
<dd>Number of rows in training data.</dd>
<dt>n_predictors</dt>
<dd>Number of predictor variables.</dd>
<dt>n_algorithms</dt>
<dd>Number of algorithms to tune.</dd>
<dt>max_time_minutes</dt>
<dd>Maximum acceptable tuning time in minutes.</dd>
<dt>tuning_strategy</dt>
<dd>Preferred tuning strategy.</dd></dl>

Arguments

Recommend Tuning Configuration — recommend_tuning_config

<dl>

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


<dt>n_predictors</dt>
<dd>Number of predictor variables.</dd>


<dt>n_algorithms</dt>
<dd>Number of algorithms to tune.</dd>


<dt>max_time_minutes</dt>
<dd>Maximum acceptable tuning time in minutes.</dd>


<dt>tuning_strategy</dt>
<dd>Preferred tuning strategy.</dd>

</dl>

recommend_tuning_config: Recommend Tuning Configuration

Description

Usage

Value

Arguments