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.
You Will Learn
- Options for inserting machine learning into the decision making of your organization
- How to use multiple models for value estimation and classification
- How to properly prepare data for different kinds of supervised models
- How data preparation must be automated in parallel with the model if deployment is to succeed
- Compare model accuracy scores to model propensity scores that drive decisions at deployment
- 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
- Analytics practitioners
- Data scientists
- IT professionals
- Technology planners
- Business analysts
- Analytics project leaders