ntucllab/libact
Pool-based active learning in Python
This tool helps data scientists and machine learning practitioners train more effective models with less labeled data. It takes your existing dataset, some of which is labeled and some unlabeled, and intelligently selects the most informative unlabeled examples for you to label. The output is a more accurate predictive model, built with fewer human labeling hours.
789 stars. Actively maintained with 1 commit in the last 30 days. Available on PyPI.
Use this if you need to build predictive models but have limited labeled data or find data labeling to be a costly and time-consuming bottleneck.
Not ideal if your dataset is small and fully labeled, or if you need to perform unsupervised learning without any human input.
Stars
789
Forks
172
Language
Python
License
BSD-2-Clause
Category
Last pushed
Feb 12, 2026
Commits (30d)
1
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