# libact: Pool-based Active Learning in Python

arXiv: https://arxiv.org/abs/1710.00379 · HTML: https://mlsanity.com/paper/1710.00379/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2017.

## Built on by (most cited first, 12 of 12)

- [A Lagrangian Duality Approach to Active Learning](https://mlsanity.com/paper/2202.04108/) (2022)
- [Small-Text: Active Learning for Text Classification in Python](https://mlsanity.com/paper/2107.10314/) (2021)
- [DeepAL: Deep Active Learning in Python](https://mlsanity.com/paper/2111.15258/) (2021)
- [Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review](https://mlsanity.com/paper/2105.01044/) (2021)
- [An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets](https://mlsanity.com/paper/2306.08954/) (2023)
- [Active Learning Under Malicious Mislabeling and Poisoning Attacks](https://mlsanity.com/paper/2101.00157/) (2021)
- [Heuristic Stopping Rules For Technology-Assisted Review](https://mlsanity.com/paper/2106.09871/) (2021)
- [PyRelationAL: a python library for active learning research and development](https://mlsanity.com/paper/2205.11117/) (2022)
- [ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP](https://mlsanity.com/paper/2308.02537/) (2023)
- [Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI](https://mlsanity.com/paper/2207.09109/) (2022)
- [regAL: Python Package for Active Learning of Regression Problems](https://mlsanity.com/paper/2410.17917/) (2024)
- [Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid](https://mlsanity.com/paper/2506.03817/) (2025)

## Abstract (arXiv)

libact is a Python package designed to make active learning easier for general users. The package not only implements several popular active learning strategies, but also features the active-learning-by-learning meta-algorithm that assists the users to automatically select the best strategy on the fly. Furthermore, the package provides a unified interface for implementing more strategies, models and application-specific labelers. The package is open-source on Github, and can be easily installed from Python Package Index repository.