mlr3tuning_assertions

assert_tuner

assert_tuners

assert_tuner_async

assert_tuner_batch

assert_tuning_instance

assert_tuning_instance_async

assert_tuning_instance_batch

Most assertion functions ensure the right class attribute, and optionally additional properties.

internal

Hyperparameter optimization package of the 'mlr3' ecosystem.
It features highly configurable search spaces via the 'paradox'
package and finds optimal hyperparameter configurations for any 'mlr3'
learner.  'mlr3tuning' works with several optimization algorithms e.g.
Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo')
and Hyperband (in 'mlr3hyperband'). Moreover, it can automatically
optimize learners and estimate the performance of optimized models
with nested resampling.

Marc Becker

mlr3tuning

Hyperparameter Optimization for 'mlr3'

Michel Lang

Jakob Richter

Bernd Bischl

Daniel Schalk

mlr3tuning_assertions function

<dl><dt>tuner</dt>
<dd>(TunerBatch).</dd>
<dt>tuners</dt>
<dd>(list of Tuner).</dd>
<dt>inst</dt>
<dd>(TuningInstanceBatchSingleCrit | TuningInstanceBatchMultiCrit).</dd></dl>

Arguments

Assertion for mlr3tuning objects — mlr3tuning_assertions

<dl>

<dt>tuner</dt>
<dd>(TunerBatch).</dd>


<dt>tuners</dt>
<dd>(list of Tuner).</dd>


<dt>inst</dt>
<dd>(TuningInstanceBatchSingleCrit | TuningInstanceBatchMultiCrit).</dd>

</dl>

Assertion for mlr3tuning objects

mlr3tuning_assertions: Assertion for mlr3tuning objects

Description

Usage

Arguments