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MLH Code of Conduct
2022 Datathon
Model Interpretability
How do we interpret the results from our models? As we develop newer and better machine learning models, they grow more and more difficult to draw results from. In this workshop, we will go over techniques for making contemporary models easier to interpret.
- tags: explainability, partial dependence, LIME, conditional expectation, SHAP
When:
Sunday Oct 18th, 5:00pm to 6:15pm (GMT)
Presented By:
Alan Feder
About the Speaker(s):
Alan Feder is a Principal Data Scientist currently working for Invesco Mutual Funds. Prior to Invesco, Alan worked for 8 years gaining actuarial and data science experience in finance. Alan has a Bachelors Degree in Mathematics & Economics from Columbia University, and a Masters in Statistics from Columbia as well.
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