# google/active-learning

Repository: https://github.com/google/active-learning
Canonical: https://ross.abutalabs.com/products/active-learning
Language: Python
License: Apache-2.0
License Family: permissive
Archived: true
Last push: 2022-12-05T01:29:18+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3253, "days_push": 1368, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1162, forks 204 (observed 2026-08-28T04:03:49.546815+00:00)

## What it is
A Python module for running experiments comparing different active learning algorithms on benchmark datasets. It provides a main experiment runner, dataset download utilities, and a collection of sampling methods that can be mixed together.

## Use cases
- compare active learning sampling strategies on benchmark datasets
- run active learning experiments with tensorflow models
- benchmark margin sampling vs uncertainty sampling
- evaluate labeling efficiency of sampling methods
- experiment with mixtures of active learning samplers
- download and preprocess datasets for active learning research

## When to choose
- you need a ready-made harness to benchmark active learning algorithms
- you want to reproduce or extend active learning research experiments
- you need many sampling methods implemented in one place

## When to avoid
- you need production active learning pipelines integrated into your ML stack
- you want a maintained library with recent updates
- you need deep learning frameworks other than TensorFlow

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, benchmarking, data-science
- domain: machine-learning, artificial-intelligence
- platform: python, windows
- tags: active-learning, experiment-framework, sampling-methods, tensorflow, research-tool, research, linux, macos

## Member repositories
- google/active-learning (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:49.546815+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 2026-08-30T06:31:38.157339+00:00, confidence not recorded.
  - readme: https://github.com/google/active-learning (fetched 2026-08-28T04:03:49.546815+00:00, sha 68d4eb9bc384)
- Data as of 2026-08-30T08:39:29.467469+00:00.