There is a new minor release of H2O that introduces two useful improvements to our XGBoost integration: interaction constraints and feature interactions. Interaction Constraints Feature interaction constraints allow users to decide which variables are allowed to interact and which are not. Potential benefits: Better predictive performance from focusing on interactions that work – whether through […]

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 them up. With the support of the open […]

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 […]

  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 solves many data science […]