Interpretable Machine Learning: Shapley Values (Seminar Slides)
24 Pages Posted: 21 Jul 2020
Date Written: June 27, 2020
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: Suggested Citation
López de Prado, Marcos and López de Prado, Marcos, Interpretable Machine Learning: Shapley Values (Seminar Slides) (June 27, 2020). Available at SSRN: https://ssrn.com/abstract=3637020 or http://dx.doi.org/10.2139/ssrn.3637020
Do you have a job opening that you would like to promote on SSRN?
Feedback to SSRN
If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday.