Abstract

Machine Learning is a powerful subset of AI and data science. One of its most effective tools is built on decision trees. The objective of this presentation is to promote Machine Learning tools for Six Sigma analytics without replacing traditional tools like multiple linear regression. Decision tree methods and multiple linear regression will be compared using the same data set. Advantages and disadvantages for both methods are given. A few cautions are noted when using big data and predictive modeling.

About Jeff

 As President and Founder of Value Creation Institute, LLC, and a Certified Executive Master Black Belt (MBB), Jeff Heslop has served as an MBB for two decades. He believes that training alone is not sufficient to truly impact business without a sound deployment strategy and process, which must be dynamically flexible to meet the exacting needs of the client.

During his distinguished career, he has served as the manager of the Lean Six Sigma (LSS) Training Team at GE Aviation, a Total Quality Advisor with GE Aviation, and a Certified Executive Master Black Belt (CEMBB) through the Six Sigma Management Institute. Through his expertise and insights, Jeff has trained and mentored thousands of students from around the world, including over 130 waves of Black Belts plus Master Black Belts, Green Belts, Design for Six Sigma Black Belts, suppliers, customers, and numerous executives. He has learned directly from some of the greatest thinkers in business and quality improvement,  such as Dr. W. Edwards Deming, Dr. Brian Joiner, Dr. Don Wheeler, and Dr. Mikel Harry, who co-founded Six Sigma.

Jeff received his bachelor’s degree in mechanical engineering from Kansas State University and his master's degree in statistics from Miami University of Ohio. He is a certified MBB through GE and a certified Executive MBB through Dr. Mikel Harry and the Six Sigma Management Institute.