August 21, 2019
2:15 pm - 5:30 pm
Duration: Half Day Course
Ensembling is one of the hottest techniques in today’s predictive analytcis competitions. Every single recent winner of Kaggle.com 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 analytic practitioners and adoptable by leadership.
This course will provide a detailed overview of ensemble models, their origin, and show why they are so effective. We will explain the building blocks of virtually all ensembles 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.
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