WATCH HERE

Presenter: Weiguang Guan, SHARCNET

In some binary classification problems, the underlying distribution of positive and negative samples are highly unbalanced. For example, fraudulent credit card transactions are rare compared to the volume of legitimate transactions. Training a classification model in such a case needs to take into account the nature of skewed distribution. In this seminar, we will develop a fraud detector which can be used to screen credit card transactions. We will describe the methods used to handle unbalanced data training.