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(This article was first published on <strong><a href="http://www.rblog.uni-freiburg.de/2017/03/07/ensemble-learning-in-r/"> R Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Previous research in data mining has devised numerous different algorithms for learning tasks. While an individual algorithm might already work decently, one can usually obtain a better predictive by combining several. This approach is referred to as ensemble learning.<br>
Common examples include random forests, boosting and AdaBost in particular.</p>
<p>Our slide deck is positioned at the intersection of teaching the basic idea of ensemble learning and providing practical insights in R.<br>
Therefore, each algorithm comes with an easy-to-understand explanation on how to use it in R.</p>
<p>We hope that the slide deck enables practitioners to quickly adopt ensemble learning for their applications in R. Moreover, the materials might lay the groundwork for courses on data mining and machine learning.</p>
<p>Download the slides <a target="_blank" rel="nofollow" href="http://www.is.uni-freiburg.de/ressourcen/business-analytics/10_ensemblelearning.pdf">here</a><br>
Download the exercise sheet <a target="_blank" rel="nofollow" href="http://www.is.uni-freiburg.de/ressourcen/business-analytics/homework_ensemblelearning_questions.pdf">here</a><br>
<em>The content was republished on <a target="_blank" rel="nofollow" href="http://www.r-bloggers.com/ensemble-learning-in-r/">r-bloggers.com</a> with permission.</em></p>
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