# MLWave/Kaggle-Ensemble-Guide

Code for the Kaggle Ensembling Guide Article on MLWave

Repository: https://github.com/MLWave/Kaggle-Ensemble-Guide
Canonical: https://ross.abutalabs.com/products/kaggle-ensemble-guide
Language: Python
License Family: other
Last push: 2017-11-16T23:43:01+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": 4100, "days_push": 3212, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1615, forks 551 (observed 2026-08-28T04:05:11.548030+00:00)

## What it is
Companion code for the MLWave Kaggle Ensembling Guide article, providing Python scripts that combine multiple model prediction CSVs via voting, weighted voting, averaging, geometric mean, and rank averaging. It also includes correlation measurement scripts to compare prediction files.

## Use cases
- ensemble predictions from multiple kaggle submission csvs
- average model predictions with weighted voting
- compute rank average across model outputs
- measure correlation between two prediction files
- learn how ensembling improves kaggle accuracy

## When to choose
- you have several prediction CSVs and want to blend them for a competition submission
- you want a simple script-based introduction to ensembling techniques

## When to avoid
- you need a maintained ML library with full ensembling pipelines
- you need weighted stacking trained on validation data
- you need a package with a license for commercial use

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, cli, developer-tools
- domain: machine-learning, data-science, tutorials
- platform: python, cli, cross-platform
- tags: ensembling, kaggle, model-averaging, voting, rank-averaging, education

## Member repositories
- MLWave/Kaggle-Ensemble-Guide (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:11.548030+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 2026-08-30T03:50:16.262083+00:00, confidence not recorded.
  - readme: https://github.com/MLWave/Kaggle-Ensemble-Guide (fetched 2026-08-28T04:05:11.548030+00:00, sha fb1bcddcd92d)
- Data as of 2026-08-30T08:39:29.467469+00:00.