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

Getting started with SHAP

In this section, we will first install SHAP. This version of SHAP includes algorithms and visualizations. The programs come mainly from Su-In Lee's lab at the University of Washington and Microsoft Research.

Once we have installed SHAP, we will import the data, split the datasets, and build a data interception function to target specific features.

Let's start by installing SHAP.

Open SHAP_IMDB.ipynb in Google Colaboratory. We will be using this notebook throughout this chapter.

Installing SHAP

You can install SHAP with one line of code:

# @title SHAP installation
!pip install shap

However, if you restart Colaboratory, you might lose this installation. You can verify if SHAP is installed with the following code:

# @title SHAP installation
try:
  import shap
except:
  !pip install shap

Next, we will import the modules we will be using.

Importing the modules

Each module has a specific prerequisite...