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Ensembling is one of the hottest techniques in today’s predictive analytics competitions. Every recent winner of Kaggle.com and KDD competitions used an ensemble technique, including famous algorithms such as XGBoost, random forest, and "deep stacking." This course will provide a detailed overview of ensemble models, their origin, and why they are so effective. The instructor will explain the building blocks of virtually all ensemble techniques: bagging, boosting, and stacking.
However, these techniques may produce “black box” models that may not be understood by decision makers. Having ensemble models that are difficult to interpret may seem like trading one problem for another, but in recent years, new solutions have been found. An explanatory layer can be built on top of black box models, including ensembles. Explainable AI (XAI) is a set of techniques for providing these explanations.
This course both prepares you to put ensemble techniques to use in your business and covers the XAI techniques that ensure understandable results. This course will discuss two kinds of explanations: global and local. Global explanations describe overall patterns in the model, notably which variables are most and least important. Local explanations describe why a particular case received a prediction. For example, why was a specific loan predicted to default? Regulated industries are often especially interested in XAI, but anyone that is considering complex machine learning models can benefit from a knowledge of XAI techniques.
Register today for TDWI's advanced predictive modeling with ensemble techniques course.
You Will Learn
- What are ensemble models and what are their advantages?
- The core elements of ensembles and their application: bagging, boosting, and stacking
- The pros and cons of complex "black box" techniques in solving business problems
- Why the popularity of black box models is on the rise and has prompted increased awareness and availability of XAI techniques
- Popular global and local explanation techniques
- Overall guidance for complex models and interpretability
- Analytics practitioners
- Data scientists
- Machine learning engineers
- IT professionals
- Technology planners
- Business analysts
- Analytics project leaders