Learning to Quantify: Inferring Unbiased Estimators of Class Prevalence via Machine Learning


Alejandro Moreo, alejandro.moreo@isti.cnr.it

Fabrizio Sebastiani, fabrizio.sebastiani@isti.cnr.it

Content and organization

This course provides an introduction and an overview of learning to quantify (a.k.a. “quantification”), i.e. the task of training estimators of class proportions in unlabeled data by means of supervised learning. In data science, learning to quantify is a task of its own related to classification yet different from it, since estimating class proportions by simply classifying all data and counting the labels assigned by the classifier is known to often return inaccurate (“biased”) class proportion estimates.

The course introduces learning to quantify by looking at the supervised learning methods that can be used to perform it, at the evaluation measures and evaluation protocols that should be used for evaluating the quality of the returned predictions, at the numerous fields of human activity in which the use of quantification techniques may provide improved results with respect to the naive use of classification techniques, and at advanced topics in quantification research.

The course is suitable to researchers, data scientists, or PhD students, who want to come up to speed with the state of the art in learning to quantify, but also to researchers wishing to apply data science technologies to fields of human activity (e.g., the social sciences, political science, epidemiology, market research) which focus on aggregate (“macro”) data rather than on individual (“micro”) data. The course also includes a hands-on part, in which the students will be guided through the implementation and/or use of quantification tools. This hands-on part also includes a brief introduction to QuaPy, a Python-based open-source library for learning to quantify.



Course Duration

8 hours

Course Type

Short Course

Participation terms

Free for everybody. Both AIDA and non-AIDA students are encouraged to participate in this short course.

If you are an AIDA Student* already, please:

Step (a): Register in the course by sending an email to the Course Lecturer alejandro.moreo@isti.cnr.it for your registration.


Step (b): Enroll in the same course in the AIDA course link (TBA) using the ‘Enroll on this course’ button below, so that this course enters your AIDA Certificate of Course Attendance.

If you are not an AIDA Student do only step (a).

*AIDA Students should have been registered in the AIDA system already (they are PhD students or PostDocs that belong only to the AIDA Members listed in this page: Members)

Lecture Plan

Tuesday, March 14, 2023 - 10:00 to 12:00 CET
Thursday, March 16, 2023 - 10:00 to 12:00 CET
Tuesday, March 21, 2023 - 10:00 to 12:00 CET
Thursday, March 23, 2023 - 10:00 to 12:00 CET



Modality (online/in person):


Host Institution
Italian National Council of Research (CNR), Institute for the Science and Technologies of Information (ISTI)

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