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Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots | Intuition and Math

Автор: A Data Odyssey

Загружено: 2024-04-29

Просмотров: 4632

Описание: Both Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots are used to understand and explain machine learning models. PDPs can tell us if a relationship between a model feature and target variable is linear, non-linear or if there is no relationship. Similarly, ICE plots are used to visualise interactions. Now, at first glance, these plots may look complicated. But you will see, they are actually constructed in a fairly intuitive way.

In this video, we will:
Take you step-by-step through how PDPs and ICE plots are created.
Discuss what insight the explainable AI methods can give
And we will end by explaining the mathematics behind PDPs.

🚀 Free Course 🚀
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XAI course: https://adataodyssey.com/courses/xai-...
SHAP course: https://adataodyssey.com/courses/shap...

🚀 Companion article with link to code (no-paywall link): 🚀
https://medium.com/data-science/the-u...

🚀 Useful playlists 🚀
XAI:    • Explainable AI (XAI)  
SHAP:    • SHAP  
Algorithm fairness:    • Algorithm Fairness  

🚀 Get in touch 🚀
Medium:   / conorosullyds  
Threads: https://www.threads.net/@conorosullyds
Twitter:   / conorosullyds  
Website: https://adataodyssey.com/

🚀 Chapters 🚀
00:00 Introduction
01:31 Understanding PDPs
04:20 Visualising relationships with PDPs
06:58 Understanding ICE Plots
06:26 The maths

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Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots | Intuition and Math

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