Classification and Regression Trees Explained

Classification and Regression Trees Explained

Classification and regression trees are a popular and effective machine learning algorithm used to interpret data and ultimately aid in making informed predictions based on that data. It's a valuable addition to your toolkit, as it helps identify patterns, understand root cause, and comprehend both categorical and continuous outputs. As one of the most essential machine learning tools, mastering the basics of a Classification and Regression tree is highly beneficial to your process improvement and data analysis initiatives.

But what are the ins and outs of Classification and Regression Trees?

When would I use it?

Why is it useful to me?

This free, hour-long webcast session will answer your questions and provide real-world application examples.

In this session, we will:

  • Provide an introduction to Classification and Regression Trees
  • Explore when to use one application over the other
  • Describe the advantages and disadvantages of C/RT against other data analysis techniques
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Roger Woodard
Roger Woodard

Academic Director, Data Science Online Master's ProgramUniversity of Notre Dame

Professor Woodard joined the University of Notre Dame in 2017 as the inaugural director of its new online M.S.-ACMS Data Science graduate program. Woodard came to Notre Dame after 14 years at North Carolina State University. At NCSU, Woodard was a teaching professor in the Department of Statistics and, since 2013, director of online programs in the Department of Statistics. Woodard has won numerous teaching awards, including the N.C. State Outstanding Teacher Award, the American Statistical Association's 2005 Waller Education Award for excellence and innovation in teaching, and was named an N.C. State Alumni Distinguished Undergraduate Professor. Roger Woodard earned a Ph.D. in statistics from the University of Missouri. When not in the classroom, he is an avid traveler and enjoys building furniture.

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