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Hands-On Explainable AI (XAI) with Python

You're reading from Hands-On Explainable AI (XAI) with Python Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps

Product type Paperback

Published in Jul 2020

Publisher Packt

ISBN-13 9781800208131

Length 454 pages

Edition 1st Edition

Languages

Python

Concepts

Artificial Intelligence

Author (1):

Denis Rothman

View More author details

Table of Contents (16) Chapters

Preface

1. Explaining Artificial Intelligence with Python

2. White Box XAI for AI Bias and Ethics FREE CHAPTER

3. Explaining Machine Learning with Facets

4. Microsoft Azure Machine Learning Model Interpretability with SHAP

5. Building an Explainable AI Solution from Scratch

6. AI Fairness with Google's What-If Tool (WIT)

7. A Python Client for Explainable AI Chatbots

8. Local Interpretable Model-Agnostic Explanations (LIME)

9. The Counterfactual Explanations Method

10. Contrastive XAI

11. Anchors XAI

12. Cognitive XAI

13. Answers to the Questions

14. Other Books You May Enjoy

15. Index

Summary

In this chapter, we explored how to explain the output of a machine learning algorithm with an agnostic model approach using SHapley Additive exPlanations (SHAP). SHAP provides an excellent way to explain models by just analyzing their input data and output predictions.

We saw that SHAP relies on the Shapley value to explain the marginal contribution of a feature in a prediction. We started by understanding the mathematical foundations of the Shapley value. We then applied the Shapley value equation to a sentiment analysis example. With that in mind, we got started with SHAP.

We installed SHAP, imported the modules, imported the dataset, and split it into a training dataset and a testing dataset. Once that was done, we vectorized the data to run a linear model. We created the SHAP linear model explainer and visualized the marginal contribution of the features of the dataset in relation to the sentiment analysis predictions of reviews. A positive review prediction value...

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Authors (1)

Denis Rothman

Denis Rothman

Expert in AI Transformers including ChatGPT/GPT-4, Bestselling Author

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