Abstract: 

Target leakage is one of the most difficult problems in developing real-world models. It occurs when training data gets contaminated with information that will not be known at prediction time. Data collection, feature engineering, partitioning, and model validation are all potential sources of data leakage. This talk offers real-life examples of data leakage at different stages of data science projects, discusses countermeasures, and lays out best practices for model validation.

Bio: 

Yuriy Guts is a Machine Learning Engineer at DataRobot with over 10 years of industry experience in data science and software architecture. His primary interests are productionalizing data science, automated machine learning, time series forecasting, and processing spoken and written language. He teaches AI and ML at UCU, competes on Kaggle, and has led multiple international data science and engineering teams.