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(This article was first published on <strong><a href="http://blog.suiji.org/post/2017/08/03/Rborist-version-0-1.8-available-from-CRAN"> Mood Stochastic</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Version 0-1.8 of the <strong>Rborist</strong> implementation of the Random<br>
Forest (TM) algorithm is now available from CRAN. Although most changes involve<br>
refactoring to accommodate future updates, there are several bug fixes and<br>
enhancements worth mentioning.</p>
<p>New option <em>maxLeaf</em> allows a limit to be set on the number of<br>
terminal nodes (i.e., leaves) in each trained tree. In order to not to<br>
introduce behavior dependent upon the training algorithm, leaves are pruned<br>
from fully-trained trees.</p>
<p>A few users had reported premature termination of their R sessions. All<br>
reproducible instances were found to originate from uncaught exceptions thrown<br>
from glue code. These are now caught.</p>
<p>An error in splitting sparse representations resulted in egregious behavior<br>
when employing autocompression. This error has been repaired.</p>
<p>There has been a redoubling of effort to ensure regular data-access<br>
patterns, particularly within nested loops. While decision-tree training is at<br>
heart an exercise in random memory access, there are tangible benefits to<br>
keeping irregular accesses to a minimum. In particular, we continue to see<br>
improvements to both the Arborist’s performance and its scalability as this<br>
focus is maintained.</p>
<p>On the now well-known flight-delay data set, for example,<br>
<strong>Rborist</strong> achieves parity with <strong>Xgboost</strong>, the<br>
acknowledged leader in performance, as the size of the training set increases.<br>
Experiments were performed on an 8-core, single-node I7 having 32GB of DDR3<br>
RAM. Two sets of timings were recorded for <strong>Rborist</strong>, one<br>
employing factors to represent categorical predictors, the other employing a<br>
one-hot encoding. Xgboost, which employs a one-hot encoding, was run with the<br>
default fast (&quot;approximate&quot;) execution mode. Comparative timings are given in<br>
the table below, with the AUC value of prediction in parentheses:</p>
<p><a rel="nofollow" href="http://blog.suiji.org/public/blowup1.pdf" target="_blank">blowup1.pdf</a></p>
<p>Finally, we note the passing of an unreported bug. As Zhao et al., note in<br>
their 2017, JSS paper<a rel="nofollow" hreflang="en" href="https://www.jstatsoft.org/article/view/v077i03" target="_blank"></a>, the initial release of <strong>Rborist</strong>, 0-1.0,<br>
&quot;fails even on the small example of ParallelForest&quot;, a census of household<br>
incomes. Although identified in 2015, the failure was never reported and we<br>
remain unclear as to its provenance. Lamentable as this state of affairs is,<br>
suffice it to say that version 0-1.8, training the example as a two-category<br>
classification model, completes in approximately 25 seconds and infers the test<br>
outcomes with an AUC value of 0.9.</p>
<p>Thanks go out to a number of people who have suggested new features,<br>
isolated bugs and corrected documentation. These include Ryan Ballantine, Brent<br>
Crossman, Chris Kennedy, Mark Juarez and Pantelis Hadjipantelis.</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/2017/08/03/Rborist-version-0-1.8-available-from-CRAN"> Mood Stochastic</a></strong>.</div>
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