libact and scikit-activeml
About libact
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.
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.
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