# h2oai/h2o-3

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Repository: https://github.com/h2oai/h2o-3
Canonical: https://ross.abutalabs.com/products/h2o-3
Homepage: http://h2o.ai
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: h2o, machine-learning, data-science, deep-learning, big-data, ensemble-learning, gbm, random-forest, naive-bayes, pca, opensource, distributed, java, python, r, hadoop, spark, gpu, automl, h2o-automl
Last push: 2026-08-26T10:05:32+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 4566, "days_push": 7, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7494, forks 2025 (observed 2026-08-28T04:10:00.320732+00:00)

## What it is
H2O-3 is an open-source, distributed, in-memory machine learning platform implementing algorithms such as GLM, GBM/XGBoost, Random Forest, Deep Learning, Stacked Ensembles, and AutoML. It is accessible from Python, R, Scala, Java, and a Flow web UI, integrates with Hadoop and Spark, and exports models as POJO/MOJO for fast production scoring.

## Use cases
- train gradient boosting models on large datasets
- run automatic machine learning to find the best model
- train deep learning models from Python or R
- score models in production with MOJO export
- run machine learning on Hadoop or Spark clusters
- build stacked ensembles of multiple models
- do PCA, K-Means, and GLM on big data

## When to choose
- you need distributed, scalable ML beyond a single machine's memory
- you want AutoML to automatically train and tune many models
- you need fast production scoring via POJO/MOJO export
- you work in Python or R but need big-data performance
- you want an Apache-licensed ML platform with Hadoop/Spark integration

## When to avoid
- you need lightweight scikit-learn-style ML on small datasets
- you need deep learning with GPU training on neural architectures like CNNs or transformers
- you want a pure-Python stack without a JVM dependency
- you need LLM fine-tuning or generative AI features

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, deep-learning, data-science, benchmarking
- domain: machine-learning, data-science, big-data, artificial-intelligence
- platform: python, jvm, cross-platform, cloud
- tags: automl, gbm, gradient-boosting, random-forest, glm, stacked-ensembles, distributed-ml, hadoop, spark, mojo-scoring, r, scala, docker

## Member repositories
- h2oai/h2o-3 (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:00.320732+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:37:33.837239+00:00, confidence not recorded.
  - readme: https://github.com/h2oai/h2o-3 (fetched 2026-08-28T04:10:00.320732+00:00, sha a2ee15fa9eff)
  - homepage: http://h2o.ai (fetched 2026-08-29T08:33:03.797063+00:00, sha 6b9a5ca9fced)
  - site_page: https://h2o.ai/docs (fetched 2026-08-29T08:33:03.821102+00:00, sha 411722578bc1)
  - site_page: https://h2o.ai/platform/enterprise-h2ogpte (fetched 2026-08-29T08:33:03.807912+00:00, sha 71f7b1d062f7)
  - site_page: https://h2o.ai/platform/why-h2o (fetched 2026-08-29T08:33:03.811062+00:00, sha dbfd58bb2a72)
  - site_page: https://h2o.ai/company (fetched 2026-08-29T08:33:03.813798+00:00, sha ddd0af08be22)
  - site_page: https://h2o.ai/company/press-media?tagFilter=Press+Release (fetched 2026-08-29T08:33:03.816327+00:00, sha 6aa60f9bcf14)
  - site_page: https://h2o.ai/partner-network/find-a-partner (fetched 2026-08-29T08:33:03.818534+00:00, sha 9d1a4050938e)
- Data as of 2026-08-30T08:39:29.467469+00:00.