# modAL: A modular active learning framework for Python

arXiv: https://arxiv.org/abs/1805.00979 · HTML: https://mlsanity.com/paper/1805.00979/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2018.

## Built on by (most cited first, 30 of 35)

- [Small-Text: Active Learning for Text Classification in Python](https://mlsanity.com/paper/2107.10314/) (2021)
- [Practical Galaxy Morphology Tools from Deep Supervised Representation Learning](https://mlsanity.com/paper/2110.12735/) (2021)
- [Hitting the Target: Stopping Active Learning at the Cost-Based Optimum](https://mlsanity.com/paper/2110.03802/) (2021)
- [Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images](https://mlsanity.com/paper/2110.07782/) (2021)
- [Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation](https://mlsanity.com/paper/2207.00845/) (2022)
- [Novel Intent Detection and Active Learning Based Classification (Student Abstract)](https://mlsanity.com/paper/2304.11058/) (2023)
- [Ground Truth Inference for Weakly Supervised Entity Matching](https://mlsanity.com/paper/2211.06975/) (2022)
- [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)
- [General Information Metrics for Improving AI Model Training Efficiency](https://mlsanity.com/paper/2501.02004/) (2025)
- [Deep reinforced active learning for multi-class image classification](https://mlsanity.com/paper/2206.13391/) (2022)
- [Seeing and Believing: Evaluating the Trustworthiness of Twitter Users](https://mlsanity.com/paper/2107.08027/) (2021)
- [Addressing practical challenges in Active Learning via a hybrid query strategy](https://mlsanity.com/paper/2110.03785/) (2021)
- [PyRelationAL: a python library for active learning research and development](https://mlsanity.com/paper/2205.11117/) (2022)
- [Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users](https://mlsanity.com/paper/2210.15214/) (2022)
- [MEGAnno: Exploratory Labeling for NLP in Computational Notebooks](https://mlsanity.com/paper/2301.03095/) (2023)
- [Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails](https://mlsanity.com/paper/2303.00870/) (2023)
- [Targeting Misalignment: A Conflict-Aware Framework for Reward-Model-based LLM Alignment](https://mlsanity.com/paper/2512.09212/) (2025)
- [AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active Learning](https://mlsanity.com/paper/2109.05207/) (2021)
- [Semantic Interactive Learning for Text Classification: A Constructive Approach for Contextual Interactions](https://mlsanity.com/paper/2209.02984/) (2022)
- [ALANNO: An Active Learning Annotation System for Mortals](https://mlsanity.com/paper/2211.06224/) (2022)
- [Are Good Explainers Secretly Human-in-the-Loop Active Learners?](https://mlsanity.com/paper/2306.13935/) (2023)
- [Active Learning Using Aggregated Acquisition Functions: Accuracy and Sustainability Analysis](https://mlsanity.com/paper/2602.07440/) (2026)
- [Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier Detection](https://mlsanity.com/paper/2104.12837/) (2021)
- [SARS-CoV-2 Result Interpretation based on Image Analysis of Lateral Flow Devices](https://mlsanity.com/paper/2205.13311/) (2022)
- [Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI](https://mlsanity.com/paper/2207.09109/) (2022)
- [Label-Efficient Interactive Time-Series Anomaly Detection](https://mlsanity.com/paper/2212.14621/) (2022)
- [Decentralized Technologies for AI Hubs](https://mlsanity.com/paper/2306.04274/) (2023)
- [Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization](https://mlsanity.com/paper/2309.02818/) (2023)
- [Pool-Based Active Learning with Proper Topological Regions](https://mlsanity.com/paper/2310.01597/) (2023)
- [ActiveClean: Generating Line-Level Vulnerability Data via Active Learning](https://mlsanity.com/paper/2312.01588/) (2023)

## Abstract (arXiv)

modAL is a modular active learning framework for Python, aimed to make active learning research and practice simpler. Its distinguishing features are (i) clear and modular object oriented design (ii) full compatibility with scikit-learn models and workflows. These features make fast prototyping and easy extensibility possible, aiding the development of real-life active learning pipelines and novel algorithms as well. modAL is fully open source, hosted on GitHub at https://github.com/cosmic-cortex/modAL. To assure code quality, extensive unit tests are provided and continuous integration is applied. In addition, a detailed documentation with several tutorials are also available for ease of use. The framework is available in PyPI and distributed under the MIT license.