Feature Engine provides Python tools for feature engineering and selection that are compatible with scikit-learn.
Open source
24 alternativesmodAL
modAL provides a modular active learning framework for Python built on top of scikit-learn.
About modAL
modAL is an open source active learning framework for Python 3. It helps users build workflows that choose which unlabelled examples to query for labels, which can reduce the labelling effort needed to improve a classifier. The framework is built on top of scikit-learn and is designed to be modular, flexible, and extensible.
Users can start with a scikit-learn classifier, such as RandomForestClassifier, in a few lines of code. The default uncertainty sampling can be replaced with built-in strategies, such as entropy sampling, or with a custom query strategy written as a Python function. The project also states that scikit-learn or Keras models can be integrated into a workflow. An active regression example is included.
Key features
- Active learning workflows built on scikit-learn
- Integration of scikit-learn or Keras models into learners
- Built-in query strategies, including entropy sampling
- Custom query strategies written as Python functions
- Replaceable uncertainty measures for query selection
- Active regression example
Best for: Python developers and researchers who want to build or customize active learning workflows around scikit-learn or Keras models.
Good for
- Modular design that allows individual components to be replaced
- Open source
- Built on scikit-learn
Watch out for
- Written for Python 3
modAL at a glance
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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modAL FAQ
Which types of models can be used with modAL?
The project states that scikit-learn or Keras models can be integrated into an active learning algorithm.
Can I write my own query strategy?
Yes. The project includes an example of a custom random sampling strategy, written as a function that takes a classifier and a pool of unlabelled instances and returns the index and instance to query.
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