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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

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800208131
Length 454 pages
Edition 1st Edition
Languages
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Toc

Table of Contents (16) Chapters Close

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

Getting started with Facets

In this section, we will install Facets in Python, using Jupyter Notebook on Google Colaboratory.

We will then retrieve the training and testing datasets. Finally, we will read the data files.

The data files are the training and testing datasets from Chapter 1, Explaining Artificial Intelligence with Python. This way, we are in a situation in which we know the subject and can analyze the data without having to spend time understanding what it means.

Let's first install Facets on Google Colaboratory.

Installing Facets on Google Colaboratory

Open Facets.ipynb. The first cell contains the installation command:

# @title Install the facets-overview pip package.
!pip install facets-overview

The installation may be lost when the virtual machine (VM) is restarted. If this is the case, it will be installed again. If Facets is installed, the following message is displayed:

Requirement already satisfied:

The program...

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