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H2O Release 3.46
We are excited to announce the release of H2O-3 3.46.0.1! Some of the highlights of this major release are that we added custom metric support for XGBoost, allowed grid search models to be sorted with custom metrics, and we enabled H2O MOJO and POJO to work with MLFlow. Several improvements were also made to the Uplift model (like MLI ...
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H2O Release 3.44
We are excited to announce the release of H2O-3 3.44.0.1! We have added and improved many items. A few of our highlights are the implementation of AdaBoost, Shapley values support, Python 3.10 and 3.11 support, and added custom metric support for Deep Learning, Uplift Distributed Random Forest (DRF), Stacked Ensemble, and AutoML. Please r...
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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 in Wave 0.24.0
Another Wave release has arrived with quite a few exciting new features. Let’s quickly go over the biggest ones.Wave init CLIHow many times you wanted to build a Wave app fast, but then you realized you need to start from scratch, copy over the skeleton of your app and work up from there? For these exact reasons, we introduced a new wave...
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H2O Release 3.36 (Zorn)
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...
Read moreNew Features Now Available with the Latest Release of the H2O AI Cloud 21.10
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 ...
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H2O Release 3.34 (Zizler)
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 the pe...
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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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H2O Release 3.32 (Zermelo)
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 framewor...
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H2O Release 3.30 (Zahradnik)
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...
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H2O Release 3.28 (Yu)
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 .Hierarchi...
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New Innovations in Driverless AI
What’s new in Driverless AIWe’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 and NLP recipes to meet the...
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H2O Release 3.26 (Yau)
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 release...
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H2O-3, Sparkling Water and Enterprise Steam Updates
We are excited to announce the new release of H2O Core, Sparkling Water and Enterprise Steam.Below are some of the new features we have added:H2O-3 Yates (3.24.0.1) – 3/31/2019Download at: http://h2o-release.s3.amazonaws.com/h2o/rel-yates/1/index.html Bug [PUBDEV-6159] – The AutoMLTest.java test suite now runs correctly on a local mach...
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H2O Release 3.24 (Yates)
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 cross-version support for model import, added new features for model interpretation, provided much-improved support for reading data from Apache Hive, and included various algorithm and AutoML impr...
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H2O New Year releases
There were two releases shortly after each other. First, on December 21st, there was a minor (fix) release 3.22.0.3 . Immediately followed by a more major release (but still on 3.22 branch) codename Xu, named after mathematician Jinchao Xu , whose work is focused on deep neural networks, besides many other fields of research.Of course, th...
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New features in H2O 3.22
Xia Release (H2O 3.22)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 introduce Isolation Forest to our portfolio of machine learning algorithms and integrates the XGBoost algorithm into our AutoML framework. The release is named after Zhihong Xia .Isolation ...
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Launching the Academic Program … OR ... What Made My First Four Weeks at H2O.ai so Special!
We just launched the H2O.ai Academic Program at our sold-out H2O World London. With nearly 1000 people in attendance, we received the first online sign-up forms submitted by professors and students alike. This program will massively democratize AI in academia, increasing the number of AI-skilled graduates – with both technical and busine...
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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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New versions of H2O-3 and Sparkling Water available
Dear H2O Community, #H2OWorld is on Monday and we can’t wait to see you there! We’ll also be live streaming the event starting at 9:25am PST. Explore the agenda here . Today we’re excited to share that new versions of H2O-3 and Sparkling Water are available. We invite you to download them here: http://www.h2o.ai/download/ H2O-3.16 – MO...
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Scalable Automatic Machine Learning: Introducing H2O's AutoML
Prepared by: Erin LeDell, Navdeep Gill & Ray Peck In recent years, the demand for machine learning experts has outpaced the supply, despite the surge of people entering the field. To address this gap, there have been big strides in the development of user-friendly machine learning software that can be used by non-experts and experts...
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Stacked Ensembles and Word2Vec now available in H2O!
Prepared by: Erin LeDell and Navdeep Gill MathJax.Hub.Config({ tex2jax: {inlineMath: [['$','$'], ['\\(','\\)']]} }); Stacked Ensembles ensemble <- h2o.stackedEnsemble(x = x, y = y, training_frame = train, base_models = my_models) Python:ensemble = H2OStackedEnsembleEstimator(base_models=my_models) ensemble.train(x=x, y=y, training...
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What is new in Sparkling Water 2.0.3 Release?
This release has H2O core – 3.10.1.2Important Feature:This architectural change allows to connect to existing h2o cluster from sparkling water. This has a benefit that we are no longer affected by Spark killing it’s executors thus we should have more stable solution in environment with lots of h2o/spark node. We are working on article on ...
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What is new in H2O latest release 3.10.2.1 (Tutte) ?
Today we released H2O version 3.10.2.1 (Tutte). It’s available on our Downloads page, and release notes can be found here . Photo Credit: https://en.wikipedia.org/wiki/W._T._Tutte Top enhancements in this release: GLM MOJO Support: GLM now supports our smaller, faster, more efficient MOJO (Model ObJect, Optimized) format for model pu...
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