Past Meetup

Deciphering the Black Box: Understanding your Machine Learning Model

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Dr. Rajiv Shah will show how interpretability tools can give you not only more confidence in a model, but also help to improve model performance. This talk will cover how you can use 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.