https://datasciencela.github.io/tag/random-forests/Recent content in random forests on DataScience.LAHugo -- gohugo.ioen-usDataScience.LA, 2014–2021 · Read-only archive: comments are closed.Sat, 21 Nov 2015 18:33:50 -0800https://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>