SHAP Values: An Overview

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In this video, I talk about SHAP values and how these can be used for explainable AI and explaining how features contribute to a machine learning's predictions for each observation. These are great tools when your goal isn't (only) prediction, but is also inference - that is, understanding the most important features that influence a response or exactly one how feature, at different levels, influences the response. Note that this is different from causal inference, which is a separate and very complex topic altogether.

SHAP value documentation:

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“But it’s been in my experience …” — I cannot express how helpful it is to hear this phrase associated with an explanation. Many ML explanations can feel overwhelming … feeling like “I have to know everything or else I am never going to land a DS job.” Bravo.

idontevenwanttomakea

I just started using SHAP, along with Python generally, a week ago, and was looking for a good summary explanation of what the output means so I can explain it all to my boss when I show findings, so this was very helpful and easy to understand!

Stevenportman-jt

This was awesome. Thanks. I’m gonna go see how I can apply these when presenting to stakeholders

rccola

hello sir, i am unable to run shap analysis in cnn regression problem, the dimension is not matching, giving only one feature importance value instead eight features.
How to resolve this problem?

goutamidas

Nice one! This is a topic I need to start getting into. Would love more content on XAI!

matteolatinov

Can I see unnecessary variables from 10 parameters that I will use to make future predictions with time series using lstm or neural network with shap?

TheSerbes

Great content and explanation Richard. Thanks!

kavitachavda

is this something we could use on sentence embeddings

cunningham.s_law

Awesome, instead of Python, can this be done using R

KN-txsd

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