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Other Automated Machine Learning (AutoML) tools and related projects:

<table>
<tr>
<th width="20%">Name</th>
<th width="15%">Language</th>
<th width="15%">License</th>
<th>Description</th>
</tr>
<tr>
<td><a href="http://www.cs.ubc.ca/labs/beta/Projects/autoweka/">Auto-WEKA</a></td>
<td>Java</td>
<td>GPL-v3</td>
<td>Automated model selection and hyper-parameter tuning for Weka models.</td>
</tr>
<tr>
<td><a href="https://github.com/automl/auto-sklearn">auto-sklearn</a></td>
<td>Python</td>
<td>BSD-3-Clause</td>
<td>An automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.</td>
</tr>
<tr>
<td><a href="https://github.com/ClimbsRocks/auto_ml">auto_ml</a></td>
<td>Python</td>
<td>MIT</td>
<td>Automated machine learning for analytics & production. Supports manual feature type declarations.</td>
</tr>
<tr>
<td><a href="http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html">H2O AutoML</a></td>
<td>Java with Python, Scala & R APIs and web GUI</td>
<td>Apache 2.0</td>
<td>Automated: data prep, hyperparameter tuning, random grid search and stacked ensembles in a distributed ML platform.</td>
</tr>
<tr>
<td><a href="https://github.com/joeddav/devol">devol</a></td>
<td>Python</td>
<td>MIT</td>
<td>Automated deep neural network design via genetic programming.</td>
</tr>
<tr>
<td><a href="https://github.com/AxeldeRomblay/MLBox">MLBox</a></td>
<td>Python</td>
<td>BSD-3-Clause</td>
<td>Accurate hyper-parameter optimization in high-dimensional space with support for distributed computing.</td>
</tr>
<tr>
<td><a href="https://github.com/RecipeML/Recipe">Recipe</a></td>
<td>C</td>
<td>GPL-v3</td>
<td>Machine-learning pipeline optimization through genetic programming. Uses grammars to define pipeline structure.</td>
</tr>
<tr>
<td><a href="https://github.com/reiinakano/xcessiv">Xcessiv</a></td>
<td>Python</td>
<td>Apache 2.0</td>
<td>A web-based application for quick, scalable, and automated hyper-parameter tuning and stacked ensembling in Python.</td>
</tr>
<tr>
<td><a href="https://github.com/PGijsbers/gama">GAMA</a></td>
<td>Python</td>
<td>Apache 2.0</td>
<td>Machine-learning pipeline optimization through asynchronous evaluation based genetic programming. </td>
</tr>
</table>