This project consists in three main tasks: first, an analysis of the current state of the art in technologies for dealing with class imbalance problems in machine learning algorithms. Second, the analysis of how this problem actually affects a particular class of statistical models, the Bayesian Classifiers, proposing
solutions to the particular problems found. And third, to implement a Bayesian Classifier and develop a series of experiments that would support the assertions of the analysis, and shed more light on how this problem can be dealt with.