To get the most out of this book

To get the most out of this book, it is recommended to:

• Focus on the key concepts of explainable AI (XAI) and how they are becoming mandatory
• Read the chapters without running the code if you wish to focus on XAI theory
• Read the chapters and run the programs if you wish to go through the theory and implementations simultaneously.

Download the example code files

You can download the example code files for this book from your account at http://www.packt.com. If you purchased this book elsewhere, you can visit http://www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at http://www.packt.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the on-screen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR / 7-Zip for Windows
• Zipeg / iZip / UnRarX for Mac
• 7-Zip / PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Explainable-AI-XAI-with-Python. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781800208131_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example; "If the label is 0, then the recommendation is to stay in the right lane."

A block of code is set as follows:

choices = str(prediction).strip('[]')
  if float(choices) <= 1:
    choice = "R lane"
  if float(choices) >= 1:
    choice = "L lane"

Command-line or terminal output is written as follows:

1 data [[0.76, 0.62, 0.02, 0.04]]  prediction: 0 class 0 acc.: True R lane
2 data [[0.16, 0.46, 0.09, 0.01]]  prediction: 0 class 1 acc.: False R lane
3 data [[1.53, 0.76, 0.06, 0.01]]  prediction: 0 class 0 acc.: True R lane

Bold: Indicates a new term, an important word, or words that you see on the screen, for example, in menus or dialog boxes, also appear in the text like this. For example: " Go to the Scatter | X-Axis and Scatter | Y-Axis drop-down lists."