modAL-python/modAL

Health v2 · maintenance only

Adoption not part of the score

2362 stars · 325 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

modAL is a modular active learning framework for Python 3, built on top of scikit-learn. It lets users rapidly assemble active learning workflows with interchangeable query strategies, uncertainty measures, and custom components.

Use cases

• reduce labeling costs by selecting the most informative samples to annotate
• run uncertainty sampling on a scikit-learn classifier
• implement custom query strategies for active learning research
• perform active regression with limited labeled data
• prototype Bayesian optimization-style sample selection loops

When to choose

• you use scikit-learn estimators and want to add active learning with minimal code
• you need modular, replaceable components to experiment with novel AL algorithms
• you want a well-documented Python library for labeling-efficiency experiments

When to avoid

• you need deep learning or GPU-based active learning out of the box
• you need a framework with frequent releases and active maintenance
• you want a turnkey annotation platform rather than a Python library

Facets

framework · maturity maintenance

machine-learning machine-learning data-science python active-learning scikit-learn bayesian-optimization query-strategies

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