Quantification

Este artigo estuda as causas de desaparecimento no estado de São Paulo por idade e sexo dos desaparecidos. Para tanto, utiliza algoritmos de aprendizado de máquina para avaliar automaticamente o texto de boletins de ocorrência.

The quantification problem consists of determining the prevalence of a given label in a target population. However, one often has access to the labels in a sample from the training population but not in the target population. A common assumption in this situation is that of prior probability shift, that is, once the labels are known, the distribution of the features is the same in the training and target populations. In this paper, we derive a new lower bound for the risk of the quantification problem under the prior shift assumption. Using a weaker version of the prior shift assumption, which can be tested, we show that ratio estimators can be used to build confidence intervals for the quantification problem.

Several machine learning applications use classifiers as a way of quantifying the prevalence of positive class labels in a target …