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

The U.S. census data problem

The U.S. census data problem uses features of the U.S. population to predict whether a person will earn more than USD 50K or not.

In this chapter, we will use WIT_Model_Comparison_Ethical.ipynb, a notebook derived from Google's notebook.

The initial dataset, adult.data, was extracted from the U.S. Census Bureau database: https://web.archive.org/web/20021205224002/https://www.census.gov/DES/www/welcome.html

Each record contains the features of a person. Several ML programs were designed to predict the income of that person. The goal was to classify the population into groups for those earning more than USD 50K and those earning less.

The adult.names file contains more information on this data and the methods.

The ML program's probabilities, as stated in the adult.names file, achieved the following accuracy:

Class probabilities for adult.all file
| Probability for the label '>50K'  : 23.93% / 24.78% (without...