modAL

Built directly on scikit-learn, modAL allows instant use of popular estimators like RandomForestClassifier and GaussianProcessRegressor, as shown in the initialization example with just a few lines of code.

Users can easily swap between built-in strategies like entropy sampling or implement custom ones with simple functions, demonstrated in the 'Replacing parts' section where a random sampling strategy is defined in a few lines.

Extends beyond classification to regression tasks, with tailored examples using Gaussian Processes and custom uncertainty measures for querying, as illustrated in the active regression workflow.

Integrates with Keras models for deep learning-based active learning, mentioned in the features and supported through examples, enabling seamless use with neural networks.