Datasets of the LeQua 2024 Learning to Quantify Data Challenge read more of Learning to quantify: LeQua 2024 dataset

This data set comprises a labelled training set used in the experimentation of the paper "Binary Quantification and Dataset Shift: An Experimental Investigation". read more of Product Reviews Dataset

QuaPy is an open source framework for quantification (a.k.a. supervised prevalence estimation, or learning to quantify) written in Python. read more of QuaPy

ql4facct is a software for replicating experiments concerning the evaluation of estimators of classifier "fairness". read more of A quantification-based method for the estimation of algorithmic fairness

This data set comprises a labeled training set, validation samples, and testing samples for ordinal quantification. It appears in our research paper "Ordinal Quantification Through Regularization", which we have published at ECML-PKDD 2022. read more of Product Reviews for Ordinal Quantification

The aim of the LeQua 2022 dataset is to allow the comparative evaluation of methods for “learning to quantify” in textual datasets, i.e., methods for training predictors of the relative frequencies of the classes of interest in sets of unlabelled textual ... read more of Learning to quantify: LeQua 2022 datasets