# WillKoehrsen/feature-selector

Feature selector is a tool for dimensionality reduction of machine learning datasets

Repository: https://github.com/WillKoehrsen/feature-selector
Canonical: https://ross.abutalabs.com/products/feature-selector
Language: Jupyter Notebook
License: GPL-3.0
License Family: copyleft
Last push: 2024-06-17T22:44:11+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": 2996, "days_push": 807, "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 2228, forks 763 (observed 2026-08-28T04:06:28.960015+00:00)

## What it is
A Python library for feature selection that identifies features to remove from machine learning datasets using five methods: missing values, single unique values, collinear features, zero importance features, and low importance features. It includes visualization tools such as correlation heatmaps and feature importance plots.

## Use cases
- remove collinear features from a dataset
- drop features with too many missing values
- identify zero importance features before training
- reduce dimensionality of a machine learning dataset
- visualize feature correlations in a dataframe
- prune low importance features to speed up model training

## When to choose
- you want a simple, notebook-friendly feature selection workflow for tabular data
- you need to inspect and visualize feature importance and collinearity before modeling
- you work with pandas DataFrames and want automated removal of redundant columns

## When to avoid
- you need feature selection for non-tabular data like text or images
- you require actively maintained tooling with modern dependency versions
- you need scalable feature selection for very large datasets beyond memory

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, data-science, data-visualization
- domain: machine-learning, data-science
- platform: python
- tags: feature-selection, dimensionality-reduction, jupyter-notebook, lightgbm, pandas

## Member repositories
- WillKoehrsen/feature-selector (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:28.960015+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 2026-08-30T02:44:41.969440+00:00, confidence not recorded.
  - readme: https://github.com/WillKoehrsen/feature-selector (fetched 2026-08-28T04:06:28.960015+00:00, sha 05b075d50592)
- Data as of 2026-08-30T08:39:29.467469+00:00.