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You're reading from  Interpretable Machine Learning with Python - Second Edition

Product typeBook
Published inOct 2023
PublisherPackt
ISBN-139781803235424
Edition2nd Edition
Concepts
Machine Learning
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Author (1)
Serg Masís
Serg Masís
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Serg Masís

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap robustly.
Read more about Serg Masís

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Comparing SHAP with LIME

As you will have noticed by now, both SHAP and LIME have limitations, but they also have strengths. SHAP is grounded in game theory and approximate Shapley values, so its SHAP values are supported by theory. These have great properties such as additivity, efficiency, and substitutability that make them consistent but violate the dummy property. It always adds up and doesn’t need parameter tuning to accomplish this. However, it’s more suited for global interpretations, and one of its most model-agnostic explainers, KernelExplainer, is painfully slow. KernelExplainer also deals with missing values by using random ones, which can put too much weight on unlikely observations.

LIME is speedy, very model-agnostic, and adaptable to all kinds of data. However, it’s not grounded on strict and consistent principles but has the intuition that neighbors are alike. Because of this, it can require tricky parameter tuning to define the neighborhood...

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Interpretable Machine Learning with Python - Second Edition
Published in: Oct 2023Publisher: PacktISBN-13: 9781803235424
© 2023 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Serg Masís

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap robustly.
Read more about Serg Masís

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