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(This article was first published on <strong><a href="http://blog.suiji.org/post/2017/05/14/Rborist-0.1-6-now-on-CRAN"> Mood Stochastic</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>The latest release of the Rborist package, which provides an accelerated<br>
implementation of the Random Forest (TM) algorithm, is available from CRAN.<br>
Version 0.1-6 offers several notable improvements:</p>
<h2>Sparse matrix representation</h2>
<p>Sparse numeric <em>dcgMatrix</em> matrix objects are now accepted as input,<br>
provided an intra-column encoding is employed. This representation is<br>
particularly useful, for example, in the case of one-hot encodings.</p>
<p>Additionally, <strong>Rborist</strong> now autocompresses training data on a<br>
per-predictor basis, compactly representing runs of arbitrary value. This<br>
space-saving feature is most useful when training iteratively, using the<br>
<em>preFormat</em> feature.</p>
<h2>Pruned representation</h2>
<p>A new option <em>thinLeaves</em> allows trained forests to be recorded in a<br>
slender format, economizing on storage.</p>
<h2>Vignette</h2>
<p>A vignette has been provided to guide users through Rborist’s various<br>
capabilities. It is hoped that this will invite more users to try the package<br>
and make it easier to use.</p>
<h2>Improved scalability</h2>
<p>Particular attention has been paid to limiting data movement and exploiting<br>
data locality. This has paid dividends in the ability of the implementation to<br>
scale across larger data sets.</p>
<p>The graph below illustrates recent progress by comparing execution times of<br>
<strong>Rborist</strong> with <strong>Xgboost</strong> on a flight-delay data<br>
set. <strong>Xgboost</strong> is considered to be among the fastest open-source<br>
packages implementing decision-tree methods. The flight-delay data, and<br>
execution scripts, are hosted on Szilard Pafka’s <strong>benchm-ml</strong><br>
project on Github<a hreflang="en" target="_blank" rel="nofollow" href="https://github.com/szilard/benchm-ml"></a> . One script was modified to extend the sample limit from 10 million<br>
to 12.5 million rows, approximately the maximum available from the data.<br>
Timings were performed on a two-socket Xeon server:</p>
<p><img title="Flight.jpg, May 2017" alt="Flight.jpg" src="https://i1.wp.com/blog.suiji.org/public/.Flight_m.jpg?w=456" data-recalc-dims="1"></p>
<p>Of particular interest is the inflection point apparent near one million<br>
rows. This is likely due to crossing a level of the memory hierarchy. That is,<br>
more and more data must be accessed from outside the L1 cache. Although<br>
<strong>Xgboost</strong> remains faster throughout this regime,<br>
<strong>Rborist</strong> appears better able to handle the transition, and the<br>
two are nearly even at 12.5 million rows, Additional testing will be needed to<br>
learn how far these scaling trends extend.</p>
<p>Thanks go out to Chris Kennedy, Christopher Brown, Carlos Ortega and Tal<br>
Galili, whose comments and contributions helped make this a successful<br>
release.</p>
<p>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://blog.suiji.org/post/2017/05/14/Rborist-0.1-6-now-on-CRAN"> Mood Stochastic</a></strong>.</div>
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