Predictive Modeling with Ensembles: Advanced Techniques for Deeper Insights
Duration: One Day Course
Ensembling is one of the hot techniques in contemporary predictive modeling competitions. Every single recent winner of competitions on Kaggle.com and in the annual KDD competitions used an ensemble technique, including famous algorithms like XGBoost or the Super Learner.
Are these competition victories paving the way for widespread organizational implementation of these techniques? This course will overview these techniques, their origin, and show why they are so effective.
We will draw inspiration from these techniques while exploring a practical approach to ensembling that is effective for most organizational problems and within reach for analytic practitioners.
What 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
- Analytic Practitioners; Data Scientists; IT Professionals; Technology Planners; Consultants; Business Analysts; Analytic Project Leaders