# kaz-Anova/StackNet

StackNet is a computational, scalable and analytical Meta modelling framework

Repository: https://github.com/kaz-Anova/StackNet
Canonical: https://ross.abutalabs.com/products/stacknet
Language: Java
License: MIT
License Family: permissive
Last push: 2018-09-19T15:19:50+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": 3461, "days_push": 2905, "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 1326, forks 336 (observed 2026-08-28T04:04:22.715626+00:00)

## What it is
StackNet is a Java-based meta-modelling framework that implements Wolpert's stacked generalization across multiple layers, resembling a feedforward neural network built one layer at a time with any supervised learning algorithm. It can be run from the command line or embedded in Java code, and leverages H2O's algorithms.

## Use cases
- stack multiple machine learning models for better accuracy
- build multi-level stacked ensembles for classification
- build stacked ensembles for regression problems
- run model stacking from the command line
- combine classifiers and regressors in a layered meta-model

## When to choose
- you want to squeeze maximum predictive accuracy via stacked generalization
- you work on the JVM and want a ready-made stacking framework
- you prefer command-line model training without writing much code

## When to avoid
- you need actively maintained tooling with recent updates
- you want a pure Python scikit-learn workflow (use pystacknet or scikit-learn instead)
- you need deep learning with backpropagation rather than layer-wise stacking

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, cli
- domain: machine-learning, data-science
- platform: jvm, cli, cross-platform
- tags: stacked-generalization, stacking, ensemble-learning, meta-modelling, java, h2o

## Member repositories
- kaz-Anova/StackNet (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:22.715626+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 2026-08-30T04:46:22.983179+00:00, confidence not recorded.
  - readme: https://github.com/kaz-Anova/StackNet (fetched 2026-08-28T04:04:22.715626+00:00, sha c5c5bf565422)
- Data as of 2026-08-30T08:39:29.467469+00:00.