Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2023

P. MishraExplainable AI Recipes https://doi.org/10.1007/978-1-4842-9029-3_2

2. Explainability for Linear Supervised Models

Pradeepta Mishra¹  

(1)

Bangalore, Karnataka, India

 

A supervised learning model is a model that is used to train an algorithm to map input data to output data. A supervised learning model can be of two types: regression or classification. In a regression scenario, the output variable is numerical, whereas with classification, the output variable is binary or multinomial. A binary output variable has two outcomes, such as true and false, accept and reject, yes and no, etc. In the case of multinomial output variables, the outcome can be more ...