NUAA-AL/ALiPy
ALiPy: Active Learning in Python is an active learning python toolbox, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods.
This project helps data scientists and machine learning researchers evaluate and compare different active learning techniques. You provide your dataset, and it helps you test various algorithms, visualize their performance, and determine which strategy best labels your data with minimal effort. It's designed for anyone working with classification models who wants to optimize their data labeling process.
899 stars. No commits in the last 6 months. Available on PyPI.
Use this if you are a data scientist or researcher exploring active learning strategies and need a flexible toolkit to benchmark different approaches on your datasets.
Not ideal if you are looking for a pre-built, production-ready system to automatically label your data without any hands-on model evaluation and comparison.
Stars
899
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116
Language
Python
License
BSD-3-Clause
Category
Last pushed
Jul 23, 2025
Commits (30d)
0
Dependencies
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