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H2O-3
Distributed machine learning and AutoML engine with Python, R, and Flow interfaces, automated model comparison, explainability, and a separate enterprise Secure edition.
About H2O-3
H2O-3 is a distributed machine learning engine for training predictive models and automating parts of model development. It includes supervised and unsupervised algorithms alongside AutoML, which trains and compares candidate models through a shared workflow.
Users can work through Python, R, JVM-based applications, or the Flow browser interface. Explainability tools provide model-level and individual-prediction views, with visualizations that help users examine model behavior and compare results.
H2O-3 is available as an Apache 2.0 open-source edition and as H2O-3 Secure, a commercial subscription. Secure adds enterprise packaging, identity controls, AutoDoc, commercial security maintenance, and support. These additions should be evaluated separately from the open-source engine.
Key Features
- Distributed Model Training: Train supported models across multiple nodes.
- Multiple Algorithms: Access methods including generalized linear models, gradient boosting, random forests, neural networks, clustering, and dimensionality reduction.
- AutoML: Automate candidate-model training and tuning within a defined workflow.
- Model Leaderboards: Compare trained candidates using their evaluation results.
- Explainability: Generate global and local explanations, variable-importance views, and partial-dependence plots.
- Multiple Interfaces: Use Python, R, Java/Scala, REST APIs, or the Flow web interface.
Pricing
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H2O-3 Open Source: Free. The core engine is distributed under the Apache 2.0 license.
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H2O-3 Secure: Contact sales. Commercial subscription with enterprise packages, AutoDoc, access controls, security maintenance, and support with SLAs. A numeric subscription price is not publicly specified on the reviewed product page.
Use Cases
- Compare predictive models through automated training and tuning.
- Build classification and regression workflows using Python or R.
- Examine model behavior with explanation plots.
- Explore datasets and models through the Flow browser interface.
Pros & Cons
Pros:
- Combines multiple algorithms and AutoML in one engine.
- Supports both code-based interfaces and a browser workspace.
- Includes explanations for individual models and model comparisons.
Cons:
- Java is required even when using the Python or R packages.
- Current documentation assigns model-artifact extraction and enterprise deployment packages to H2O-3 Secure.
Integrations
Python, R, Java, Scala, pandas, scikit-learn, Jupyter
FAQ
What is H2O-3 used for?
H2O-3 is used to train predictive models, run automated model comparisons, and examine model behavior. It supports distributed machine learning and multiple programming interfaces.
Is H2O-3 open source?
Yes. The core H2O-3 engine is available under the Apache 2.0 license.
What does H2O AutoML automate?
H2O AutoML automates training and tuning across candidate models and presents results through a leaderboard. Users still need to prepare appropriate data and evaluate whether results suit their application.
Can H2O-3 be used from Python and R?
Yes. Official installation instructions cover Python and R. Java remains a prerequisite for running the engine.
What is H2O Flow?
Flow is H2O-3's browser-based interface for working with data, building models, and inspecting results.
How does H2O-3 explain predictions?
Its explainability interface supports global model explanations and local explanations for individual rows, using visualizations such as variable importance and partial-dependence plots.
Does open-source H2O-3 include AutoDoc?
No. The official product page identifies AutoDoc as part of H2O-3 Secure.
Last edited
November 6, 2026 at 11:11 AM by Venkatraman Chandrasekaran
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