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H2O Releases 3.40.0.1 and 3.42.0.1
Our new major releases of H2O are packed with new features and fixes! Some of the major highlights of these releases are the new Decision Tree algorithm, the added ability to grid over Infogram, an upgrade to the version of XGBoost and an improvement to its speed, the completion of the maximum likelihood dispersion parameter and its expan...
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New Improvements in H2O 3.32.0.2
There is a new minor release of H2O that introduces two useful improvements to our XGBoost integration: interaction constraints and feature interactions.Interaction ConstraintsFeature interaction constraints allow users to decide which variables are allowed to interact and which are not.Potential benefits: Better predictive performance...
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What does NVIDIA’s Rapids platform mean for the Data Science community?
Today NVIDIA announced the launch of the RAPIDS suite of software libraries to enables GPU acceleration for data science workflows and we’re excited to partner with NVIDIA to bring GPU accelerated open source technology for the machine learning and AI community. “Machine learning is transforming businesses and NVIDIA GPUs are speeding...
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New features in H2O 3.18
Wolpert Release (H2O 3.18)There’s a new major release of H2O and it’s packed with new features and fixes! We named this release after David Wolpert , who is famous for inventing Stacking (aka Stacked Ensembles ). Stacking is a central component in H2O AutoML , so we’re very grateful for his contributions to machine learning! He is also fa...
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XGBoost in the H2O Machine Learning Platform
The new H2O release 3.10.5.1 brings a shiny new feature – integration of the powerful XGBoost library algorithm into H2O Machine Learning Platform! XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable. XGBoost provides parallel tree boosting (also known as GBDT, GBM) that ...
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