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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🚀 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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