Data di Pubblicazione:
2021
Abstract:
QuaPy is an open-source framework for performing quantification (a.k.a. supervised prevalence estimation), written in Python. Quantification is the task of training quantifiers via supervised learning, where a quantifier is a predictor that estimates the relative frequencies (a.k.a. prevalence values) of the classes of interest in a sample of unlabelled data. While quantification can be trivially performed by applying a standard classifier to each unlabelled data item and counting how many data items have been assigned to each class, it has been shown that this "classify and count" method is outpermsngformed by methods specifically designed for quantification. QuaPy provides implementations of a number of baseline methods and advanced quantification methods, of routines for quantification-oriented model selection, of several broadly accepted evaluation measures, and of robust evaluation protocols routinely used in the field. QuaPy also makes available datasets commonly used for testing quantifiers, and offers visualization tools for facilitating the analysis and interpretation of the results. The software is open-source and publicly available under a BSD-3 licence via GitHub, and can be installed via pip.
Tipologia CRIS:
04.01 Contributo in Atti di convegno
Keywords:
Quantification; Learning to quantify
Elenco autori:
Esuli, Andrea; MOREO FERNANDEZ, ALEJANDRO DAVID; Sebastiani, Fabrizio
Link alla scheda completa:
Link al Full Text:
Titolo del libro:
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management