Download H2o version 3.10.0.8

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 (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), Author: Erin LeDell, Navdeep Gill, Spencer Aiello, Anqi Fu, Arno Candel, Cliff Click, Tom Kraljevic, Tomas N. Fetchces a single tree of a H2O model. This function is intended to be used on Gradient Boosting Machine models or Distributed Random Forest models. h2o.h. Calculates Friedman and Popescu's H statistics, in order to test for the presence of an interaction between specified variables in h2o gbm and xgb models. H varies from 0 to 1. While most of this README is written for developers who do their own builds, most H2O users just download and use a pre-built version. If you are a Python or R user, the easiest way to install H2O is via PyPI or Anaconda (for Python) or CRAN (for R): Python pip install h2o R bltadwin.rues("h2o").

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Currently, the only version of R that is known to be incompatible with H2O is R version (codename “Spring Dance”). If you are using that version, we recommend upgrading the R version before using H2O. To check which version of H2O is installed in R, use versions::bltadwin.runs("h2o"). Version Repository Usages Date; x. Central: 9: Oct, 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 (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), ANOVA.