Deciphering the Black Box: Understanding your Machine Learning Model by Rajiv Shah
This talk will show how interpretability tools can give you not only more confidence in a model, but also help to improve model performance. This workshop will cover best practices for using techniques such as feature importance, partial dependence, and explanation approaches, such as LIME. Along the way, we will consider issues like spurious correlation, multicollinearity, and other issues that may affect model interpretation and performance. The talk will use easy to understand examples and references to open source algorithms to illustrate the techniques.
Rajiv Shah is a data scientist with DataRobot and an Adjunct Assistant Professor at the University of Illinois at Chicago. He has previously worked as a data scientist for State Farm and Caterpillar. He is an active member of the data science community in Chicago. He has a PhD from the University of Illinois at Urbana Champaign.