PDPs and ICE Plots | Python Code | scikit-learn Package

Both Partial Dependence Plots (PDPs) and Individual Conditional Expectation (ICE) plots are a popular explainable AI (XAI) method. They can visualise the relationships used by a machine learning model to make predictions. In this video, we will see how to apply the methods using Python. We will use the scikit-learn package and the PartialDependenceDisplay & partial_dependence functions.
We will see that this allows us to easily visualise the plots including:
- PDPs for individual features
- 2-dimensional PDPs
- Custom ICE Plots
- ICE Plots for categorical features
- ICE Plots for binary target variables
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🚀 Companion article with link to code (no-paywall link): 🚀
towardsdatascience.com/the-ul...
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🚀 Chapters 🚀
00:00 Introduction
00:51 Application with scikit-learn
02:21 Applying PDPs
08:22 Custom ICE Plot
09:48 2D PDPs
10:54 Categorical features
11:47 Binary target variables

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Signup here: mailchi.mp/40909011987b/signup
XAI course: adataodyssey.com/courses/xai-with-python/
SHAP course: adataodyssey.com/courses/shap-with-python/