A Python package for Quantification

The ultimate Python toolkit for class prevalence estimation. Robust methods, advanced metrics, and seamless scikit-learn integration.

Estimate prevalence by aggregating individual classifications. Includes CC, ACC, PCC, and more.

Direct methods that bypass individual classification, optimizing global loss functions directly.

Ensemble techniques and meta-learning approaches to boost quantification performance.

Dedicated metrics for quantification tasks: Absolute Error, KL Divergence, and more.

Assess the reliability of your predictions with robust confidence interval estimation.

Tools for hyperparameter tuning and selecting the best quantifier for your data.