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R Interface for the 'H2O' Scalable Machine Learning Platform.


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).

Maintainer: Erin LeDell
Author(s): 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]

License: Apache License (== 2.0)

Uses: jsonlite, RCurl, Matrix, ggplot2, mlbench, rgl, survival, data.table, slam, bit64, plot3Drgl
Reverse suggests: DALEX, DALEXtra, exprso, iml, lime, localICE, mlr, modelplotr, NeuralSens, stremr, vip
Reverse enhances: texreg

Released about 1 month ago.

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