Often rarely-happening events become the focus of a predictive analytics project. For example, fraudulent card transactions and web ad clicks are events that happen seldom but to businesses they mean opportunities to learn.
Nonetheless, modeling them is a daunting task. For instance, machine learning models might always conclude every event is not abnormal in their attempts to minimize errors. Even though some of techniques such as re-sampling can alleviate the issue, if data is too imbalanced, these could still fail.
The talk presents a novel approach to the problem. Basically, the approach is a brute-force method that finds all combinations of values that lead to substantial sample size with many anomaly cases. It uses BigData to shorten the calculation time. Pros, cons and use cases will be discussed during the talk.
Ken Park is a CEO of Knowru, a company providing useful tools to data scientists and developers. Before he started Knowru, he led teams of data scientists and fraud investigators to fight fraud in a leading online finance company. He studied Engineering at Northwestern University for his Bachelor's and Computer Science at the University of Chicago for his Master's. He likes to travel and complete marathon and triathlon events.