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Interpretable Machine Learning with Python

You're reading from   Interpretable Machine Learning with Python Learn to build interpretable high-performance models with hands-on real-world examples

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800203907
Length 736 pages
Edition 1st Edition
Languages
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Author (1):
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Serg Masís Serg Masís
Author Profile Icon Serg Masís
Serg Masís
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction to Machine Learning Interpretation
2. Chapter 1: Interpretation, Interpretability, and Explainability; and Why Does It All Matter? FREE CHAPTER 3. Chapter 2: Key Concepts of Interpretability 4. Chapter 3: Interpretation Challenges 5. Section 2: Mastering Interpretation Methods
6. Chapter 4: Fundamentals of Feature Importance and Impact 7. Chapter 5: Global Model-Agnostic Interpretation Methods 8. Chapter 6: Local Model-Agnostic Interpretation Methods 9. Chapter 7: Anchor and Counterfactual Explanations 10. Chapter 8: Visualizing Convolutional Neural Networks 11. Chapter 9: Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis 12. Section 3:Tuning for Interpretability
13. Chapter 10: Feature Selection and Engineering for Interpretability 14. Chapter 11: Bias Mitigation and Causal Inference Methods 15. Chapter 12: Monotonic Constraints and Model Tuning for Interpretability 16. Chapter 13: Adversarial Robustness 17. Chapter 14: What's Next for Machine Learning Interpretability? 18. Other Books You May Enjoy

Conventions used

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

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Next, we can adversarially train the model by first initializing a new KerasClassifier with the robust_model."

A block of code is set as follows:

base_classifier = KerasClassifier(model=base_model,\                                  clip_values=(min_, max_))y_test_mdsample_prob = np.max(y_test_prob[sampl_md_idxs],\                                                       axis=1)y_test_smsample_prob = np.max(y_test_prob[sampl_sm_idxs],\                                                       axis=1)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

robust_classifier = KerasClassifier(model=robust_model,\                                    clip_values=(min_, max_))attacks = BasicIterativeMethod(robust_classifier, eps=0.3,\                               eps_step=0.01, max_iter=20)trainer = AdversarialTrainer(robust_classifier, attacks, ratio=0.5)trainer.fit(X_train, ohe.transform(y_train), nb_epochs=30,\            batch_size=128)

Any command-line input or output is written as follows:

$ mkdir css
$ cd css

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Tips or important notes

Appear like this.

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