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<div>Update: I had a stupid error in my input. (My target value was coming across as a factor, not a float, so every value was a different class. Whoops!) </div>
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<div>That said, I’d still be interested if there is an answer to this question.</div>
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<span style="font-weight:bold">From: </span>Alessandro <<a href="mailto:alessandro.gagliardi@glassdoor.com">alessandro.gagliardi@glassdoor.com</a>><br>
<span style="font-weight:bold">Date: </span>Wednesday, December 11, 2013 at 11:59 AM<br>
<span style="font-weight:bold">To: </span>"<a href="mailto:starcluster@mit.edu">starcluster@mit.edu</a>" <<a href="mailto:starcluster@mit.edu">starcluster@mit.edu</a>><br>
<span style="font-weight:bold">Subject: </span>Training a neural net (or other classifier) across StarCluster<br>
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<div>I would like to train a neural net (or similar classifier) to predict one probabilistic value from 9 principal components. When I do it in R (using nnet) it caps at a few hundred observations, but that seems too small a sample when I have over 40k cases.
I know that the space of machine learning algorithms (distributed and otherwise) is vast and so was wondering if there was something that the StarCluster community might recommend. (Ideally it would be something that I could set up on StarCluster with minimal
difficulty.)</div>
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<div>Thanks in advance,</div>
<div><a name="_MailAutoSig" style="font-size: 15px;">-Alessandro Gagliardi</a></div>
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