Topic: "Using machine learning to predict rare events"Speaker: Weiguang Guan, SHARCNETVideo link --- 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.---The Compute Ontario Colloquia are weekly Zoom presentations on Advanced Research Computing, High Performance Computing, Research Data Management, and Research Software topics, delivered by staff from three Compute Ontario consortia (CAC, SciNet, SHARCNET) and guest speakers. The series began January 2023 and superseded similar series previously delivered by individual consortia (e.g. General Interest Seminars by SHARCNET or User Group Meeting TechTalks by SciNet). The colloquia are one hour long and include time for questions. No registration is required. Presentations are usually recorded and uploaded to the hosting consortium video channel (colloquia hosted by SHARCNET go to our youtube channel).