# datumbox/datumbox-framework

Datumbox is an open-source Machine Learning framework written in Java which allows the rapid development of Machine Learning and Statistical applications.

Repository: https://github.com/datumbox/datumbox-framework
Canonical: https://ross.abutalabs.com/products/datumbox-framework
Homepage: http://www.datumbox.com/
Language: Java
License: Apache-2.0
License Family: permissive
Topics: machine-learning, java, big-data, statistics, nlp, data-science
Last push: 2023-11-30T09:26:29+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4337, "days_push": 1007, "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 1084, forks 279 (observed 2026-08-28T04:03:31.449340+00:00)

## What it is
Datumbox is an open-source Machine Learning framework written in Java that enables rapid development of ML and statistical applications. It provides a large collection of machine learning algorithms, statistical tests, and NLP tools designed to handle large datasets.

## Use cases
- build machine learning applications in java
- run statistical tests on large datasets
- train classification and regression models on the jvm
- perform text analysis and nlp tasks
- develop sentiment analysis or spam detection models
- add machine learning to existing java projects via maven

## When to choose
- you need a pure-Java ML library with no native dependencies
- you want a broad set of classical ML algorithms and statistical methods
- you work with large datasets on the JVM
- you prefer a lightweight alternative to Spark MLlib for single-node workloads

## When to avoid
- you need deep learning or GPU acceleration
- you want an actively developed project with frequent releases
- you need Python ecosystem tooling and integrations
- you require modern transformer-based NLP models

## Facets
- artifact type: framework
- maturity: maintenance
- function: machine-learning, nlp, data-science
- domain: machine-learning, data-science, big-data
- platform: jvm, cross-platform
- tags: statistics, classification, regression, maven, jvm-ml, natural-language-processing

## Member repositories
- datumbox/datumbox-framework (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:31.449340+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 2026-08-30T06:50:01.417368+00:00, confidence not recorded.
  - readme: https://github.com/datumbox/datumbox-framework (fetched 2026-08-28T04:03:31.449340+00:00, sha 162d28a4bf0b)
  - homepage: http://www.datumbox.com/ (fetched 2026-08-29T12:52:52.114729+00:00, sha ccd4cdb67fd9)
  - site_page: https://blog.datumbox.com/author/bbriniotis (fetched 2026-08-29T12:52:52.126633+00:00, sha f43c75b68439)
- Data as of 2026-08-30T08:39:29.467469+00:00.