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TDWI Chicago Update

At TDWI, we have been working hard to navigate this ever-changing landscape in the face of COVID-19, and we want to assure you that the health and well-being of our employees, customers, and vendor partners is our top priority. Therefore, due to the growing concern around the coronavirus (COVID-19), and in alignment with the guidelines laid out by the CDC and WHO, we have decided to merge this year’s TDWI Chicago Conference (May 10-15) with TDWI Orlando 2020 (November 8-13), where it can be a successful experience for everyone. The Chicago 2020 agenda will be replicated at TDWI Orlando 2020.

Our registration team will be in contact with individual registrants and sponsors directly.

Course Description

M6A Decision Trees in Machine Learning: Building Explainable Models

May 11, 2020

9:00 am - 12:15 pm

Duration: Half Day Course

Level: Intermediate to Advanced

Prerequisite: None

Keith McCormick

Senior Consultant and Trainer

The Modeling Agency

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 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

Geared To

  • Analytics practitioners
  • Data scientists
  • IT professionals
  • Technology planners
  • Consultants
  • Business analysts
  • Analytics project leaders