Introducing H2O Lagrange (2.6.0.11) to R

Key takeaways

H2O introduced the Lagrange (2.6.0.11) release to R and data science users, describing it as an open-source distributed in-memory data analysis tool for large datasets.
The Lagrange release provided scalable solutions for Generalized Linear Model, K-Means, Random Forest, Principal Components Analysis, Summary, Gradient Boosted Regression and Classification, Naive Bayes, and Deep Learning.
The post said the h2o R package was cross-referenced on CRAN's High-Performance and Parallel Computing and Machine and Statistical Learning task views.

From my perspective the most important event that happened atuseR! 2014 was that I got to meetthe 0xdata team and now, long story short,here I am introducing the latest version of H2 O, labeledLagrange (2.6.0.11) ,to the R and greater data science communities. Beforejoining 0xdata, I was working at a competitor on a rival project and wasrepeatedly asked why my generalized linear model analytic didn’t run as fast asH2 O’s GLM. The answer then as it is now is the same – becauseH2 O has a cutting edge distributed in-memory parallel computingarchitecture – but I no longer receive an electric shock every time I say so.

For those hearing about H2 O for the first time, it is an open-sourcedistributed in-memory data analysis tool designed for extremely large data setsand the H2 O Lagrange (2.6.0.11) release provides scalable solutionsfor the followinganalysis techniques :

In my first blog post at 0xdata, I wanted to keep it simple and make sure Rusers know how to get the h2o package, which is cross-referenced on theHigh-Performance and Parallel Computing andMachine and Statistical Learning CRAN Task Views , up and running on theircomputers. To so do, open an R console of your choice and type

After you are done experimenting with the demos in R, you can open up a webbrowser to http://localhost:54321/ to give the H2 O web interface aonce over and then hop over to0xdata’s YouTube channel for somein-depth talks. Over the coming weeks we at 0xdata will continue toblog about how to use H2 Othrough R and other interfaces. If there is a particular use case you would liketo see addressed, join ourh2ostream Google Groupsconversation or e-mail us at support@0xdata.com . Until then, happy analyzing. Related Blogs R-bloggers

Key takeaways

H2O introduced the Lagrange (2.6.0.11) release to R and data science users, describing it as an open-source distributed in-memory data analysis tool for large datasets.
The Lagrange release provided scalable solutions for Generalized Linear Model, K-Means, Random Forest, Principal Components Analysis, Summary, Gradient Boosted Regression and Classification, Naive Bayes, and Deep Learning.
The post said the h2o R package was cross-referenced on CRAN's High-Performance and Parallel Computing and Machine and Statistical Learning task views.