Book Image

Hands-On Explainable AI (XAI) with Python

By : Denis Rothman
Book Image

Hands-On Explainable AI (XAI) with Python

By: Denis Rothman

Overview of this book

Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI.
Table of Contents (16 chapters)
14
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15
Index

Introduction to SHAP

SHAP was derived from game theory. Lloyd Stowell Shapley gave his name to this game theory model in the 1950s. In game theory, each player decides to contribute to a coalition of players to produce a total value that will be superior to the sum of their individual values.

The Shapley value is the marginal contribution of a given player. The goal is to find and explain the marginal contribution of each participant in a coalition of players.

For example, each player in a football team often receives different amounts of bonuses based on each player's performance throughout a few games. The Shapley value provides a fair way to distribute a bonus to each player based on her/his contribution to the games.

In this section, we will first explore SHAP intuitively. Then, we will go through the mathematical explanation of the Shapley value. Finally, we will apply the mathematical model of the Shapley value to a sentiment analysis of movie reviews.

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