The visual interpretation of decision trees. Terence Parr -- USF
Details
This talk will be posted to our YouTube Channel at https://www.youtube.com/channel/UCN0kf0sI01-FXPZdWAA-uMA after the seminar. Coffee is provided at the in-person seminar
Title: The visual interpretation of decision trees
Subtitle: How to lead a fulfilling life by being dissatisfied
Speaker: Terence Parr (http://parrt.cs.usfca.edu)
Affiliation: University of San Francisco
Abstract: Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. This talk illustrates how a new Python package called dtreeviz advances the state-of-the-art in the visual interpretation of decision trees. We’ll begin with a discussion of how decision trees work so that we're all on the same page. The talk will include details of the design decisions coauthor Prince Grover (USF MSDS ’18) and I made. I’ll finish up with some advice about how dissatisfaction can spur innovation.
Bio: Terence Parr is a professor of computer science and was founding director of the MS in data science program at the University of San Francisco. While he is best known for creating the ANTLR parser generator, Terence actually started out studying neural networks in grad school (1987). After 30 years of parsing, he's back to machine learning and recently launched http://explained.ai to help others retool as machine language practitioners!