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(This article was first published on <strong><a href="https://longhowlam.wordpress.com/2016/12/10/dont-give-up-on-single-trees-yet-an-interactive-tree-with-microsoft-r/"> R – Longhow Lam's Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<h2><img sizes="(max-width: 440px) 100vw, 440px" alt="tree" class="wp-image-2076" src="https://i0.wp.com/longhowlam.files.wordpress.com/2016/12/tree.png?w=456&ssl=1" data-recalc-dims="1"></h2>
<h2>Introduction</h2>
<p>A few days ago Microsoft announced their new <a target="_blank" rel="nofollow" href="http://blogs.technet.microsoft.com/machinelearning/2016/12/07/introducing-microsoft-r-server-9-0/">Microsoft R Server 9.0 version</a>. Among a lot of new things, it includes some new and improved machine learning algorithms in their MicrosoftML package.</p>
<ul>
<li>Fast linear learner, with support for L1 and L2 regularization. Fast boosted decision tree. Fast random forest. Logistic regression, with support for L1 and L2 regularization.</li>
<li>GPU-accelerated Deep Neural Networks (DNNs) with convolutions. Binary classification using a One-Class Support Vector Machine.</li>
</ul>
<p>And the nice thing is, the MicrosoftML package is now also available in the Microsoft R client version, which you can <a target="_blank" rel="nofollow" href="http://aka.ms/rclient">download and use for free</a>.</p>
<h2>Don’t give up on single trees yet….</h2>
<p>Despite all the more modern machine learning algorithms, a good old single decision tree can still be useful. Moreover, in <strong>a business analytics context</strong> they can still keep up in predictive power. In the last few months I have created different predictive response and churn models. I usually just try different learners, logistic regression models, single trees, boosted trees, several neural nets, random forests. In my experience a single decision tree is usually <em>‘not bad’</em>, often only slightly less predictive power than the more fancy algorithms.</p>
<p>An important thing in analytics is that you can ‘<strong>sell</strong>‘ your predictive model to the business. A single decision tree is a good way to to do just that, and with an <strong>interactive decision tree</strong> (created by Microsoft R) this becomes even more easy.</p>
<p>Here is an example: a decision tree to predict the survival of Titanic passengers.</p>
<div><img src="https://i1.wp.com/media.licdn.com/mpr/mpr/AAEAAQAAAAAAAAiSAAAAJDM0Y2VjNjljLTg3M2UtNGNlZS1iMWU2LWYzMDUzZGJhNDg5Mw.png?w=456&ssl=1" data-recalc-dims="1"></div>
<div><img src="https://i0.wp.com/media.licdn.com/mpr/mpr/AAEAAQAAAAAAAAf5AAAAJGEyZmMzY2U3LTIzMTktNDgyMy05NjMyLTg2MTc4NDQwYjgxYw.png?w=456&ssl=1" data-recalc-dims="1"></div>
<p>The interactive version of the decision tree can be found on <a target="_blank" rel="nofollow" href="http://rawgit.com/longhowlam/titanicTree/master/tree.html">my GitHub</a>.</p>
<p>Cheers, Longhow</p>
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