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		<div class="page-header"><h1>h2oEnsemble R Package</h1></div>
		<div class="well small">Permalink: <a class="more" href="/h2oensemble-r-package.html">2015-11-22 23:30:00-08:00</a>
by <a class="url fn" href="/author/admin.html">admin </a>
 in <a href="/category/software.html">Software</a>
tags: <a href="/tag/r.html">R</a> <a href="/tag/ensemble-learning.html">ensemble learning</a> <a href="/tag/machine-learning.html">machine learning</a> </div>
		<div><p>The <a href="https://github.com/h2oai/h2o-3/tree/master/h2o-r/ensemble">h2oEnsemble</a> R package provides functionality to create ensembles from the base learning algorithms that are accessible via the <a href="https://cran.r-project.org/web/packages/h2o/index.html">h2o R package</a> (H2O version 3.0 and above). The <a href="https://github.com/h2oai/h2o-3">H2O</a> machine learning software features distributed implementations of many popular machine learning methods that can be run on a local machine or a cluster. </p>
<p>This type of ensemble learning is called "super learning", "stacked regression" or "stacking." The Super Learner algorithm learns the optimal combination of the base learner fits. In a 2007 article titled, <a href="http://dx.doi.org/10.2202/1544-6115.1309">"Super Learner"</a>, it was shown that the super learner ensemble represents an asymptotically optimal system for learning. </p>
<p>A tutorial for the h2oEnsemble package is available <a href="http://learn.h2o.ai/content/tutorials/ensembles-stacking/index.html">here</a>.</p>
<p><strong>Author(s)</strong>: Erin LeDell</p>
<p><a href="https://github.com/h2oai/h2o-3/tree/master/h2o-r/ensemble">GitHub</a></p></div>
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