# h2oai/h2o-2

Please visit https://github.com/h2oai/h2o-3 for latest H2O

Repository: https://github.com/h2oai/h2o-2
Canonical: https://ross.abutalabs.com/products/h2o-2
Language: Java
License: Apache-2.0
License Family: permissive
Archived: true
Last push: 2024-10-24T17:35:57+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4956, "days_push": 678, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2249, forks 547 (observed 2026-08-28T04:06:30.854599+00:00)

## What it is
H2O-2 is a Java-based distributed machine learning and math engine that scales statistics and predictive modeling over Big Data platforms like Hadoop. It offers algorithms such as Random Forest, GLM, logistic regression, and k-Means accessible via R, REST, and JSON APIs, and has been superseded by H2O-3.

## Use cases
- run machine learning models on hadoop big data
- train random forest and glm models at scale
- do predictive modeling from R or REST API
- explore large datasets with an R-like parser
- score models online in real time
- distributed k-means clustering on big datasets

## When to choose
- maintaining an existing H2O-2 deployment
- you specifically need the legacy H2O-2 algorithms or interfaces

## When to avoid
- starting a new machine learning project
- you want the actively developed H2O platform
- you need modern algorithms like deep learning or AutoML

## Facets
- artifact type: application
- maturity: maintenance
- function: machine-learning, data-science, http-server
- domain: machine-learning, data-science, big-data, analytics
- platform: jvm, windows, cross-platform
- tags: hadoop, bigdata, glm, random-forest, k-means, r-integration, rest-api, legacy, superseded-by-h2o-3, linux, macos

## Member repositories
- h2oai/h2o-2 (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:30.854599+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 2026-08-30T02:43:54.550130+00:00, confidence not recorded.
  - readme: https://github.com/h2oai/h2o-2 (fetched 2026-08-28T04:06:30.854599+00:00, sha 0a3fabe28ee6)
- Data as of 2026-08-30T08:39:29.467469+00:00.