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

Training the model and making predictions

In this section, we will decide whether we will take the AutoML experiment's choice into account or not. Then we will run the final model chosen, train it, and finalize the prediction process.

The interactive choice of classifier

The notebook now displays a form where you can specify to activate the automatic process or not.

If you choose to set AutoML to On in the dropdown list, then the best model of the AutoML experiment will become the default model of the Notebook.

If not, choose Off in the AutoML dropdown list:

Figure 8.1: Activating AutoML or manually selecting a classifier

If you select Off, select the model you wish to choose for the LIME explainer in the dropdown list:

Figure 8.2: Selecting a classifier

Double-click on the form to view the code that manages the automatic process and the interactive selection:

# @title Activate the AutoML mode or
# choose a classifier in the...