modAL
A modular active learning framework for Python built on scikit-learn, enabling rapid creation of custom workflows.
What is modAL?
modAL is a modular active learning framework for Python designed to facilitate the creation of flexible and extensible active learning workflows. It integrates seamlessly with scikit-learn and allows users to replace components with custom solutions, making it ideal for designing novel algorithms in scenarios where labeling data is costly.
Data scientists and machine learning engineers working on classification or regression tasks where obtaining labeled data is expensive or time-consuming, such as in sentiment analysis, bioimage analysis, or any domain requiring intelligent data labeling.
Developers choose modAL for its modularity and seamless scikit-learn integration, enabling rapid prototyping of custom active learning algorithms with minimal code. Its flexibility allows easy swapping of models, uncertainty measures, and query strategies, including support for Keras and active regression.
Overview
A modular active learning framework for Python
Use Cases
Best For
- Building custom active learning workflows with scikit-learn estimators like RandomForestClassifier.
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