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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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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

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...
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