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Interpretable Machine Learning with Python

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Hosted By
Ji D. and Sou-Cheng T. C.

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Full Title: Interpretable Machine Learning with Python — What qualities make for a highly rated chocolate bar?

Abstract:
In the first part of this workshop, we will provide some context to what value interpretation can provide to machine learning practitioners. In a nutshell: models learn from our data, and we can learn a lot from our models… but only if we interpret them!
In part two, we kick off the more hands-on portion of the workshop! We will train classification models with chocolate bar ratings to identify which ones are highly recommended by chocolate experts: one SVM model on tabular data and a LightGBM NLP model. We will then employ popular model-agnostic interpretation methods to interpret these "black-box" models' decisions such as SHAP and Local Interpretable Model-Agnostic Explanations (LIME). That way, chocolatiers can understand what features correlate the most with these high ratings.

Short Bio:
Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a search engine startup, incubated by Harvard Innovation Labs. Serg is passionate about providing the often-missing link between data and decision-making. His book titled "Interpretable Machine Learning with Python" is scheduled to be released in early 2021 by UK-based publisher Packt.

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