SHAP for Binary and Multiclass Target Variables | Code and Explanations for Classification Problems
SHAP values give the contribution of a feature to a prediction made by a machine learning model. This is also true when we use SHAP for classification. For binary target variables, we interpret these values in terms of log odds. For multiclass targets, we use softmax. In this video, we will:
Discuss the interpretations of SHAP for classification problems
Give the Python code for displaying SHAP plots for categorical target variables
Explore new ways of aggregating SHAP values for multiclass targets
This knowledge will become an essential part of your explainable AI (XAI) arsenal!
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🚀 Companion Article (no-paywall link): 🚀
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🚀 Previous tutorial and other useful articles: 🚀
Intro to SHAP: https://towardsdatascience.com/introd...
Maths behind Shapley Values: https://towardsdatascience.com/from-s...
Limitations of SHAP: https://towardsdatascience.com/the-li...
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