AutoxgbResult

Result of <code><a rd-options="" href="/link/autoxgboost?package=autoxgboost&version=0.0.0.9000" data-mini-rdoc="autoxgboost::autoxgboost">autoxgboost</a></code>. A list containing the following elements:<dl class="dl-horizontal">
 <dt>optim.result [<code><a rd-options="mlrMBO" href="/link/MBOSingleObjResult?package=autoxgboost&version=0.0.0.9000&to=mlrMBO" data-mini-rdoc="mlrMBO::MBOSingleObjResult">MBOSingleObjResult</a></code>]</dt><dd>Optimization result object. See: <code><a rd-options="mlrMBO" href="/link/MBOSingleObjResult?package=autoxgboost&version=0.0.0.9000&to=mlrMBO" data-mini-rdoc="mlrMBO::MBOSingleObjResult">MBOSingleObjResult</a></code></dd>
 <dt>final.learner [<code><a rd-options="mlr" href="/link/Learner?package=autoxgboost&version=0.0.0.9000&to=mlr" data-mini-rdoc="mlr::Learner">Learner</a></code>]</dt><dd>Xgboost learner with best found hyper paramater configuration.</dd>
 <dt>final.model [<code><a rd-options="mlr" href="/link/WrappedModel?package=autoxgboost&version=0.0.0.9000&to=mlr" data-mini-rdoc="mlr::WrappedModel">WrappedModel</a></code> | <code>NULL</code>]</dt><dd>If <code>build.final.model=TRUE</code> in <code><a rd-options="" href="/link/autoxgboost?package=autoxgboost&version=0.0.0.9000" data-mini-rdoc="autoxgboost::autoxgboost">autoxgboost</a></code> a mlr model build by the full dataset and <code>final.learner</code>.</dd>
 <dt>measure [<code><a rd-options="mlr" href="/link/Measure?package=autoxgboost&version=0.0.0.9000&to=mlr" data-mini-rdoc="mlr::Measure">Measure</a></code>]</dt><dd>Measure used for optimization.</dd>
</dl>

Automatic tuning and fitting of 'xgboost'. Use early stopping
to determine the optimal number of iterations and Bayesian optimization (from
'mlrMBO') for all further parameters. Tunes class weights and thresholds in classification.
Categorical features are handled efficiently either by impact encoding or dummy encoding based on the number of factor levels.

Janek Thomas

autoxgboost

Automatic tuning and fitting of xgboost

AutoxgbResult function

Result of <code><a rd-options='' href='autoxgboost'>autoxgboost</a></code>. A list containing the following elements:<dl class='dl-horizontal'>
 <dt>optim.result [<code><a rd-options='mlrMBO' href='MBOSingleObjResult'>MBOSingleObjResult</a></code>]</dt><dd>Optimization result object. See: <code><a rd-options='mlrMBO' href='MBOSingleObjResult'>MBOSingleObjResult</a></code></dd>
 <dt>final.learner [<code><a rd-options='mlr' href='Learner'>Learner</a></code>]</dt><dd>Xgboost learner with best found hyper paramater configuration.</dd>
 <dt>final.model [<code><a rd-options='mlr' href='WrappedModel'>WrappedModel</a></code> | <code>NULL</code>]</dt><dd>If <code>build.final.model=TRUE</code> in <code><a rd-options='' href='autoxgboost'>autoxgboost</a></code> a mlr model build by the full dataset and <code>final.learner</code>.</dd>
 <dt>measure [<code><a rd-options='mlr' href='Measure'>Measure</a></code>]</dt><dd>Measure used for optimization.</dd>
</dl>

AutoxgbResult: Result of a autoxgboost call.

Description

Arguments