# reiinakano/xcessiv

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

Repository: https://github.com/reiinakano/xcessiv
Canonical: https://ross.abutalabs.com/products/xcessiv
Homepage: http://xcessiv.readthedocs.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: machine-learning, ensemble-learning, stacked-ensembles, scikit-learn, data-science, hyperparameter-optimization, automated-machine-learning
Last push: 2018-06-06T22:23:37+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3466, "days_push": 3010, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1265, forks 107 (observed 2026-08-28T04:04:10.960003+00:00)

## What it is
Xcessiv is a web-based Python application for building, tuning, and managing stacked machine learning ensembles. It provides a GUI for defining base learners, running parallel hyperparameter searches, and exporting ensembles as standalone Python code.

## Use cases
- build stacked ensembles for kaggle competitions
- automate hyperparameter tuning of scikit-learn models
- compare hundreds of model-hyperparameter combinations
- run parallel hyperparameter searches across multiple cores
- export a stacked ensemble as a standalone python file
- automate ensemble construction with greedy model selection

## When to choose
- you want a GUI-driven workflow for stacked ensembling with scikit-learn-compatible models
- you need to track and compare many model-hyperparameter combinations in one place
- you want automated hyperparameter search via Bayesian optimization and TPOT integration

## When to avoid
- you need actively maintained software - the last release was in 2018
- you work outside the scikit-learn API ecosystem
- you need deep learning or GPU-based model training workflows

## Facets
- artifact type: application
- maturity: abandoned
- function: machine-learning, data-science, gui
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: stacked-ensembles, hyperparameter-optimization, automl, scikit-learn, bayesian-optimization, tpot, web-server

## Member repositories
- reiinakano/xcessiv (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:10.960003+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 2026-08-30T05:03:53.792710+00:00, confidence not recorded.
  - readme: https://github.com/reiinakano/xcessiv (fetched 2026-08-28T04:04:10.960003+00:00, sha 62fd096e64cf)
  - registry_pypi: https://pypi.org/pypi/xcessiv/json (fetched 2026-08-29T12:16:47.973850+00:00, sha 8a39236dc856)
- Data as of 2026-08-30T08:39:29.467469+00:00.