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On-Demand Webinar

Ensemble Modeling for Machine Learning

Determining the most effective algorithm to use for a given problem domain and data set can be a daunting and seemingly futile effort.

Ensemble modeling serves to construct a collection of diverse models that yield a single, more accurate resultant model. This gives machine learning practitioners a powerful tool for generating more robust and effective models.

In this installment of our Machine Learning series, SAS Senior Data Scientist Brett Wujek will discuss the topic of ensemble modeling and answer common questions, including:

  • What are ensemble models and why are they important?
  • What are some common applications of ensemble modeling?
  • How is ensemble modeling implemented in practice?

Meet the Speaker

Bret Wujek

Brett Wujek, PhD, is a Senior Data Scientist with the R&D team in the SAS Advanced Analytics division. He helps promote and guide the direction of advanced analytics development at SAS, particularly in the areas of machine learning and data mining. Prior to joining SAS, Wujek led the development of process integration and design exploration technologies at Dassault Systèmes, helping to design and implement industry-leading, computer-aided optimization software for product design applications. His formal background is in design optimization methodologies, receiving his PhD from the University of Notre Dame for his work developing efficient algorithms for multidisciplinary design optimization.

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