R Interface for 'H2O'

R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as generalized linear models, gradient boosting machines (including xgboost), random forests, deep neural networks (deep learning), stacked ensembles, naive bayes, cox proportional hazards, k-means, PCA, word2vec, as well as a fully automatic machine learning algorithm (AutoML).


News

Changes in v3.7.0.99999 (In development on GitHub)

NEW FEATURES

  1. Example feature #1

  2. Example feature #1

BUG FIXES

  1. Example big fix #1

  2. Example big fix #1

Changes in v3.6.0.8 (On CRAN 24 Sep 2015)

NEW FEATURES

  1. Example feature #1

  2. Example feature #1

BUG FIXES

  1. Example big fix #1

  2. Example big fix #1

Reference manual

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install.packages("h2o")

3.20.0.2 by Erin LeDell, 5 days ago


https://github.com/h2oai/h2o-3


Report a bug at https://0xdata.atlassian.net/projects/PUBDEV


Browse source code at https://github.com/cran/h2o


Authors: Erin LeDell [aut, cre], Navdeep Gill [aut], Spencer Aiello [aut], Anqi Fu [aut], Arno Candel [aut], Cliff Click [aut], Tom Kraljevic [aut], Tomas Nykodym [aut], Patrick Aboyoun [aut], Michal Kurka [aut], Michal Malohlava [aut], Ludi Rehak [ctb], Eric Eckstrand [ctb], Brandon Hill [ctb], Sebastian Vidrio [ctb], Surekha Jadhawani [ctb], Amy Wang [ctb], Raymond Peck [ctb], Wendy Wong [ctb], Jan Gorecki [ctb], Matt Dowle [ctb], Yuan Tang [ctb], Lauren DiPerna [ctb], H2O.ai [cph, fnd]


Documentation:   PDF Manual  


Task views: Machine Learning & Statistical Learning, High-Performance and Parallel Computing with R, Model Deployment with R


Apache License (== 2.0) license


Imports graphics, tools, utils, RCurl, jsonlite

Depends on methods, stats

Suggests ggplot2, mlbench, Matrix, slam, bit64, data.table, survival

System requirements: Java (>= 7)


Imported by rsparkling.

Suggested by exprso, lime, mlr, vip.

Enhanced by texreg.


See at CRAN