scikit-activeml and ALiPy
About scikit-activeml
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.
About ALiPy
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.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work