https://datasciencela.github.io/tag/h2o/Recent content in h2o on DataScience.LAHugo -- gohugo.ioen-usDataScience.LA, 2014–2021 · Read-only archive: comments are closed.Mon, 04 Jan 2016 22:18:18 -0800https://datasciencela.github.io/pydsla-erin-ledell-presents-intro-to-machine-learning-with-h2o-and-python/Tue, 05 Jan 2016 09:30:53 -0800https://datasciencela.github.io/pydsla-erin-ledell-presents-intro-to-machine-learning-with-h2o-and-python/<p>Join PyDSLA for a talk by Erin LeDell Intro to Machine Learning with H2O and Python</p> <p>Get your tickets <a href="https://www.eventbrite.com/e/pydsla-erin-ledell-presents-intro-to-machine-learning-with-h2o-and-python-tickets-20449298402">HERE</a></p> <p>Summary:</p> <p>This workshop will provide an overview of how to use H2O, the scalable open source machine learning library, from Python.  The core algorithms of H2O are implemented in Java, however, fully-featured APIs are available in R, Python, Scala, and also through a web interface. The focus of this hands-on workshop will be the &ldquo;h2o&rdquo; Python module.</p>https://datasciencela.github.io/benchmarking-random-forest-implementations/Tue, 19 May 2015 08:30:44 -0700https://datasciencela.github.io/benchmarking-random-forest-implementations/<p>I currently have the need for machine learning tools that can deal with observations of the order of 10 millions in the context of binary classification. That kind of data is a few GBs in size and it fits comfortably nowadays in the RAM of a decent single machine. It is a trivial task for linear models, as there are plenty of open source tools that can train a logistic regression with this amount of data on a single machine in a few seconds, even while using only 1 processor core (many of these tools are single-threaded). Linear models are also the <a href="http://arxiv.org/abs/1110.4198">gold standard</a> of large-scale machine learning that can run on clusters, processing very large distributed datasets.</p>