Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks

© 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_4

4. Explainability for Non-Linear Models

Pradeepta Mishra¹  

(1)

Sobha Silicon Oasis, Bangalore, Karnataka, India

 

This chapter explores the use of LIME, SHAP, and Skope-rules explainable AI-based Python libraries to explain the decisions made by non-linear models for supervised learning tasks with structured data. In this chapter, you are going learn various ways of explaining non-linear and tree-based models and their decisions in predicting the dependent variable. In a supervised machine learning task, there is a target variable, which is also known as a dependent variable ...