Interpretable Machine Learning: Shapley Values (seminar slides)

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See all articles by Marcos Lopez de Prado

Marcos Lopez de Prado

Cornell University - Operations Research & Industrial Engineering; True Positive Technologies

Date Written: June 27, 2020

Abstract

Machine learning (ML) algorithms utilize the power of computers to solve tasks that are beyond the grasp of classical statistical methods. However, ML is often perceived as a black-box, hindering its adoption.

This seminar demonstrates the use of Shapley values to interpret the outputs of ML models. With the help of interpretability methods, ML is becoming the primary tool of scientific discovery, through induction as well as abduction.

Keywords: Machine learning, interpretability, deduction, induction, abduction, attribution

JEL Classification: G0, G1, G2, G15, G24, E44

Suggested Citation

López de Prado, Marcos, Interpretable Machine Learning: Shapley Values (seminar slides) (June 27, 2020). Available at SSRN: https://ssrn.com/abstract=

Marcos López de Prado (Contact Author)

Cornell University - Operations Research & Industrial Engineering ( email )

237 Rhodes Hall
Ithaca, NY 14853
United States

HOME PAGE: http://www.orie.cornell.edu

True Positive Technologies ( email )

NY
United States

HOME PAGE: http://www.truepositive.com

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