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Why Building Model Ensembles is a Game Changer

May 26

Free

Session Time and Date

Friday, May 26th from 3pm – 4pm CDT

 

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Presentation Description

The most effective approach to win predictive analytics data competitions and producing highly accurate predictive models is the use of model ensembles, a technique that combines predictions from multiple models into a single score. The use of ensembles has revolutionized predictive modeling not just in competitions, such as the Netflix Prize, Kaggle, and KDD CUP competitions, but also in everyday modeling for private and public sector organizations. This talk introduces model ensembles and will walk through the history of model ensembles in machine learning and predictive analytics, including Bagging, Boosting, Random Forests, Stochastic Gradient Boosting, and heterogeneous ensembles. While ensembles appear to be more complex than individual models, thus violating Occams Razor, this talk will also unravel the apparent contradiction. Real-world examples of the application of model ensembles will be provided throughout the talk.

Details

Date:
May 26
Cost:
Free
Event Categories:
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Other

Primary Speaker First Name
Dean
Primary Speaker Last Name
Abbott
Primary Speaker Bio
Dean Abbott is Co-Founder and Chief Data Scientist of SmarterHQ. Mr. Abbott is an internationally recognized data mining and predictive analytics expert with three decades of experience applying advanced data mining algorithms, data preparation techniques, and data visualization methods to real-world problems, including customer analytics, fraud detection, risk modeling, text mining, survey analysis, planned giving, and many more. Mr. Abbott is the author of Applied Predictive Analytics (Wiley, 2014) and co-author of IBM SPSS Modeler Cookbook (Packt Publishing, 2013). He is a highly-regarded and popular keynote and technical track speaker at Predictive Analytics and Data Mining conferences worldwide, and is on the Advisory Boards for the UC/Irvine Predictive Analytics Certificate as well as the UCSD Data Mining Certificate programs. He has a B.S. in Mathematics of Computation from Rensselaer (1985) and a Master of Applied Mathematics from the University of Virginia (1987).
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