modAL-python/modAL

A modular active learning framework for Python

48
/ 100
Emerging

This tool helps data scientists and machine learning engineers build better performing models using less labeled data. It takes your existing unlabeled datasets and a base machine learning model, then intelligently identifies the most informative data points for you to manually label. The output is a highly efficient active learning workflow that improves your model's accuracy with minimal labeling effort.

2,342 stars. No commits in the last 6 months.

Use this if you have a large dataset with many unlabeled examples and the cost of manually labeling data is a significant bottleneck in developing your predictive models.

Not ideal if you already have perfectly balanced, fully labeled datasets readily available or if your machine learning tasks do not involve high costs for data annotation.

data-labeling machine-learning-engineering data-annotation-optimization model-training predictive-analytics
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 22 / 25

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Stars

2,342

Forks

327

Language

Python

License

MIT

Last pushed

Feb 26, 2024

Commits (30d)

0

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