Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots | Intuition and Math
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 🚀 Signup here: https://mailchi.mp/40909011987b/signup 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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