This open access book 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 book 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 book 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.
