scikit-activeml/scikit-activeml
scikit-activeml: A Comprehensive and User-friendly Active Learning Library
This library helps machine learning practitioners efficiently train models when labeled data is scarce or expensive to obtain. You provide a large amount of unlabeled data and a small initial set of labeled data. The system intelligently selects the most informative data points for you to label, resulting in a high-performing model with minimal labeling effort. Data scientists and ML engineers working with limited labeling budgets would find this valuable.
186 stars and 6,789 monthly downloads. Available on PyPI.
Use this if you need to build high-accuracy machine learning models but face constraints on the amount of data you can afford to manually label.
Not ideal if you already have abundant labeled data or are working with very small datasets where active learning benefits would be negligible.
Stars
186
Forks
22
Language
Python
License
BSD-3-Clause
Category
Last pushed
Mar 26, 2026
Monthly downloads
6,789
Commits (30d)
0
Dependencies
6
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/scikit-activeml/scikit-activeml"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Related frameworks
ntucllab/libact
Pool-based active learning in Python
python-adaptive/adaptive
:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions
NUAA-AL/ALiPy
ALiPy: Active Learning in Python is an active learning python toolbox, which allows users to...
ai4co/awesome-fm4co
Recent research papers about Foundation Models for Combinatorial Optimization
baal-org/baal
Bayesian active learning library for research and industrial usecases.