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TDWI San Diego Update

At TDWI, we have been working hard to navigate this ever-changing landscape in the face of COVID-19, and we want to assure you that the health and well-being of our employees, customers, and vendor partners is our top priority. Therefore, we have unfortunately decided to cancel TDWI San Diego 2020.

We truly appreciate your support during this difficult time. Our registration team will be in contact with individual registrants and sponsors directly. View our virtual learning options at

Course Description

W5P Predictive Modeling with Ensembles: Advanced Techniques for Deeper Insights

August 19, 2020

2:15 pm - 5:30 pm

Duration: Half Day Course

Level: Intermediate to Advanced

Prerequisite: None

Keith McCormick

Senior Consultant and Trainer

The Modeling Agency

Ensembling is one of the hottest techniques in today’s predictive analytcis competitions. Every single recent winner of and KDD competitions used an ensemble technique, including famous algorithms such as XGBoost, Random Forest, and "Deep Stacking."

Are these competition victories paving the way for widespread organizational implementation of these techniques? Yes, but not entirely. We will walk through an effective and practical approach to ensembling most applicable to organizational problems, attainable by analytics practitioners and adoptable by leadership.

This course will provide a detailed overview of ensemble models, their origin, and why they are so effective. We will explain the building blocks of virtually all ensemble techniques: bagging, boosting, and stacking.

While not a prerequisite, attending the "Decision Trees in Machine Learning" course provides a great foundation of machine learning and supervised learning techniques prior to this session.

You Will Learn

  • What are ensemble models and what are their advantages?
  • Why are ensembles in the news?
  • Three influential ensembling approaches and three famous algorithms
  • The core elements of ensembles and their application – bagging, boosting, and stacking
  • How to apply “meta-modeling” to real-world problems
  • The pros and cons of complex "black box" techniques in solving business problems
  • The challenge of applying competition strategies to organizational problems
  • Case Study: Using an ensemble to address systematically missing data

Geared To

  • Analytics practitioners
  • Data scientists
  • IT professionals
  • Technology planners
  • Consultants
  • Business analysts
  • Analytics project leaders