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From the Publisher
Why is machine learning interpretability important and how will this book help you learn about it?
With AI systems replacing or complementing human decision-makers with machine learning models designed for the most complex tasks, trust is mission-critical. And thus, understanding how your ML model generates an outcome by complying with the principles of interpretable ML ensures the reliability of the ML model.
This book is a comprehensive hands-on guide to all things machine learning interpretability, presenting its topics with the help of real-world examples. Interpretable Machine Learning with Python takes you through the fundamentals and challenges in interpretation to help you design your systems with fairness, accountability, and transparency – the core principles of interpretable ML synonymous with Explainable Artificial Intelligence (XAI). This book will help you to mitigate the risks associated with poor predictions.
Topics covered
Why Does Interpretability Matter?
White Box and Glass Box Models
Permutation Feature Importance, Partial Dependence Plots, SHAP, and LIME
Anchor and Counterfactual Explanations
Visualizing Convolutional Neural Networks
Bias Mitigation Methods
Adversarial Robustness
And more…
What makes this book different from other books on interpretable machine learning?
Interpretable Machine Learning with Python is an extensive guide that tackles both sides of the equation: the diagnosis and the treatment of interpretability concerns. It goes beyond transparency to cover fairness and accountability, which are often ignored or underplayed by most practitioner-oriented books on the topic.
This book is mission-centric. Every chapter takes you on a journey to discover a wide range of topics using case studies that are as realistic as possible. Therefore, ‘toy datasets’ such as MNIST, Iris, and Titanic, which are too clean to depict real-world conditions, are not included.
How has your experience helped you to write this book?
In my 15 years of development experience, I’ve learned that for a product to be adopted and embraced, you have to trust it, and to trust it, you have to understand it.
In web development in particular, even if a website is up 99.9% of the time, stakeholders remember more the times that the website was down than those times it wasn’t. I realized how important it was to explain predictions and assure a degree of reliability or, at least, anticipate points of failure. However, unlike software, complex ML models can’t be debugged line by line. Even ML models with high predictive performance still get it wrong sometimes, and understanding the ways they could fail can help improve outcomes or, at least, manage expectations.
This book allows you to look under the hood and demystify the “black-box” ML model so that you can make assurances to stakeholders and mitigate issues such as overfitting, unfair outcomes, uncertainty, and lack of adversarial robustness.
What do you want readers to take away from Interpretable Machine Learning with Python?
Interpretation is often seen as an essential skill for descriptive analytics; however, it’s also very much leveraged in predictive and prescriptive analytics. With this book, you’ll realize that training a good machine learning model is more than just optimizing predictive performance. The goodness of a model can be measured in many ways, such as those encompassed by concepts of fairness and robustness. Interpretable machine learning is not limited to a toolset for making complex models explainable, instead you can learn from a model and improve it in more ways than with predictive performance. Interpretable ML is also how Ethical AI, Responsible AI, and Fair AI are implemented by ML practitioners.
Publisher : Packt Publishing (March 26, 2021)
Language : English
Paperback : 736 pages
ISBN-10 : 180020390X
ISBN-13 : 978-1800203907
Item Weight : 2.77 pounds
Dimensions : 9.25 x 7.5 x 1.51 inches
Customers say
Customers find the book helpful for understanding interpretable machine learning. It provides detailed examples and explanations for applying state-of-the-art methods. They appreciate the valuable contribution to understanding biases in ML, as well as the gold nuggets of information spread throughout the book. Overall, readers describe it as an excellent resource that integrates the fundamentals of ML evaluation metrics and is suitable for modern coders.
AI-generated from the text of customer reviews
Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples
Are you interested in understanding how machine learning models make decisions and predictions? Want to build models that are not only accurate but also interpretable? Look no further than this post!
In this post, we will explore the concept of interpretable machine learning and how it can help you build models that are not only accurate but also easy to understand and explain. We will cover key concepts and techniques for building interpretable models, including feature importance, model explanation, and model visualization.
Using Python and popular machine learning libraries such as scikit-learn and XGBoost, we will walk through hands-on examples that demonstrate how to build interpretable models for real-world datasets. By the end of this post, you will have the knowledge and skills to build high-performance models that are not only accurate but also interpretable.
So, if you’re ready to take your machine learning skills to the next level and build models that are both accurate and interpretable, then this post is for you. Let’s dive in and start building interpretable machine learning models with Python!
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