Decision trees 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 organizational resources.
New users of these established techniques are often impressed with how easy it is to develop decision trees since automated model-building software is widely available. However, proper data preparation is necessary to get optimal results.
This half-day session will dedicate half of its time to translating the business problem into a form that the algorithms can support and preparing data for optimal performance during modeling. The second half of the course focuses on different decision tree algorithms for classification and regression. Participants may consider “Predictive Modeling with Ensembles” as a natural follow-on to this session.
You Will Learn
- Options for inserting machine learning into the decision making process 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
- How to compare model accuracy scores to model propensity scores that drive decisions at deployment
- Analytic practitioners, data scientists, IT professionals, technology planners, consultants, business analysts, analytics project leaders