The “Christmas Market Effect”: A Case of Overfitting

A balanced dataset should be able to provide a comprehensive representation of the domain in which we carry out classification, but it is not trivial to make sure that the classifier gets the right amount of information in each class, not only in terms of quantity but in terms of knowledge and content. In the context of a terrorist incident classifier, I will talk about a case of overfitting on underrepresented features, the early attempts of mitigating the features that carry an undesirably strong signal and a suggestion for addressing the problem systematically by assessing the salience of words in the dataset.