Introduction to Predictive Performance Assessment

Assessing predictive performance is a critical step when building prediction models (of any kind), requiring the selection of one or more performance measures and an appropriate validation method to estimate the performance that would be achieved by the model on the population of interest. In this webinar, you will learn about:

• Binary, Categorical and Continuous Outcome Performance Measures
• Performance Curves (ROCs and P-R Curves)
• Validation Methods (Hold-Out Set, Cross-Validation)
• Validation Method Extensions and Alternatives
• Calibration.

This is the 2nd webinar in a 3-part series focused on the machine learning. Other workshops in this series:

• Oct 13, Introduction to Machine Learning
• Nov 3, Introduction to Neural Networks and Deep Learning

About the Machine Learning Series

This webinar series was made possible via joint collaboration between Faculty of Science (FOS), Applied Statistics and Data Science Group (ASDa) and Graduate Pathways for Success Program (GPS). It was originally funded by the UBC Science Strategic Innovation Fund and is now currently funded by the Graduate Pathways for Success Program. Don’t let data overwhelm you! Join us for this machine learning webinar series and use our expert guidance to empower yourself with deeper understanding of data!

Why attend?
Machine learning and Statistics are dynamic scientific disciplines that enable reaching meaningful insights from data, it’s not just about numbers. To produce reliable results, you need to justify the choice of the applied statistical or machine learning methods and models, as well as validate the underlying assumptions.

What to expect?

This webinar series provides introductions to the foundational concepts of advanced statistical methodologies in machine learning algorithms. We will provide practical insights by discussing different machine learning methods, their appropriate application and how to assess their predictive performance. The aim is to equip you with a deeper understanding of the key concepts of statistical and machine learning methodology, rather than solving specific project problems. While R code for hands-on guidance may be provided, the emphasis of this series will be on the methodological aspects. Each webinar is a self-contained introduction, but as topics become increasingly complex with each consecutive webinar, some aspects will be built on concepts taught in the previous sessions. Hence, there is benefit in attending all the webinars in this series.

ABOUT ASDA

The Applied Statistics and Data Science Group in the UBC Department of Statistics participates in collaborative research and provides statistical consulting services. ASDa expertise includes problem formulation, translation of research questions into testable statistical hypotheses, design of experiments and sampling plans for surveys, the choice and explanation of statistical methodology, statistical computing and graphics, the interpretation of findings and more. ASDa also plays an active role in continuing statistical education on and off the UBC campus, giving seminars, webinars, hands-on workshops and courses on statistical concepts and methodologies to various departments, research groups and at teaching hospitals. If you are a graduate student and have questions about your specific project, please see this website to book a one-hour free statistical consultation.

Facilitator

Nikolas Krstic is a PhD graduate student at the Department of Statistics and a part-time Statistical Consultant with ASDa. While pursuing his previous degrees, he worked as a statistical analyst at the British Columbia Centre for Disease Control (BCCDC), authoring several published papers on a wide range of environmental health topics. Over the past few years, he has worked with numerous clients on projects from a variety of different disciplines. During his studies, research and consulting work, he has developed a strong background in regression analysis, statistical learning, and general statistical problem solving.