This chapter explores the use of the SHAP, LIME, SKATER, and ELI5 libraries to explain the decisions made by linear models for supervised learning tasks for structured data. In this chapter, you are going to learn various ways of explaining the linear models and their decisions. In supervised machine learning tasks, there is a target variable, which is also known as a dependent variable, and a set of independent variables. The objective is to predict the dependent ...
© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2022
P. MishraPractical Explainable AI Using Pythonhttps://doi.org/10.1007/978-1-4842-7158-2_33. Explainability for Linear Models
Pradeepta Mishra1
(1)
Sobha Silicon Oasis, Bangalore, Karnataka, India
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