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 […]

There’s a new major release of H2O, and it’s packed with new features and fixes! Among the big new features in this release are Distributed Uplift Random Forest, an algorithm typically used in marketing and medicine to model uplift, and Infogram, a new research direction in machine learning that focuses on interpretability and fairness in Admissible […]

The Makers here at H2O.ai have been busy building new features and enhancing capabilities across our AI platform. Designed to support our core mission of democratizing AI, these additions to our platform simplify the ability to make AI you can trust, operate it efficiently and innovate with ready-made AI applications. Launched in January of 2021, […]

There’s a new major release of H2O, and it’s packed with new features and fixes! Among the big new features in this release, we’ve added Extended Isolation Forest for improved results on anomaly detection problems, and we’ve implemented the Type III SS test (ANOVAGLM) and the MAXR method to GLM. For existing algorithms, we improved […]

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 […]

There’s a new major release of H2O, and it’s packed with new features and fixes! Among the big new features in this release, we’ve added RuleFit — an interpretable machine learning algorithm, introduced a new toolbox for model explainability, made Target Encoding work for all classes of problems, and integrated it in our AutoML framework. On […]

There’s a new major release of H2O, and it’s packed with new features and fixes! Among the big new features in this release, we’ve introduced support for Generalized Additive Models, added an option to build many models in parallel on segments of your dataset, improved support for deploying on Kubernetes, upgraded XGBoost with newly added features, […]

There’s a new major release of H2O, and it’s packed with new features and fixes! Among the big new features in this release, we’ve introduced support for Hierarchical GLM, added an option to parallelize Grid Search, upgraded XGBoost with newly added features, and improved our AutoML framework. The release is named after Bin Yu. Hierarchical GLM […]

What’s new in Driverless AI We’re super excited to announce the latest release of H2O Driverless AI. This is a major release with a ton of new features and functionality. Let’s quickly dig into all of that:  Make Your Own AI with Recipes for Every Use Case: In the last year, Driverless AI introduced time-series […]

There’s a new major release of H2O, and it’s packed with new features and fixes! Among the big new features in this release, we’ve introduced the ability to define a Custom Loss Function in our GBM implementation, and we’ve extended the portfolio of our machine learning algorithms with the implementation of the SVM algorithm. The […]