September 23, 2020
9:00 am - 5:00 pm
Duration: Full Day Course
Senior Consultant and Trainer
The Modeling Agency
Decision trees and ensemble models provide powerful predictive insights. These data-driven insights inform which forces are shaping your organization’s outcomes. Once built, the models can produce key indicators to optimize the allocation of resources.
New users of decision tree techniques are often impressed with how easy they are to develop since automated model-building software is widely available. However, proper data preparation is necessary for optimal results. In this course, you will learn to translate the business problem into a form that the algorithms can support and to prepare data for optimal performance during modeling. You will then learn different decision tree algorithms for classification and regression.
Ensembling is one of the hottest techniques in today’s predictive analytics 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 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, explain their origin, and show why they are so effective. You will learn the building blocks of virtually all ensemble techniques: bagging, boosting, and stacking.