Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples

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Publisher ‏ : ‎ Packt Publishing; 2nd ed. edition (October 31, 2023)
Language ‏ : ‎ English
Paperback ‏ : ‎ 606 pages
ISBN-10 ‏ : ‎ 180323542X
ISBN-13 ‏ : ‎ 978-1803235424
Item Weight ‏ : ‎ 2.29 pounds
Dimensions ‏ : ‎ 1.36 x 7.5 x 9.25 inches

Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples

In the world of machine learning, building models that are not only accurate but also interpretable, fair, and robust is crucial. With the increasing reliance on AI and ML systems in various industries, the need for transparency and accountability in these models has never been higher.

In this post, we will explore how to achieve these goals using Python, a popular programming language for data science and machine learning. We will cover techniques and tools that can help you build models that are not only high-performing but also easy to understand, fair, and resistant to various types of biases.

Some of the topics we will cover include:

– Interpretable machine learning techniques such as feature importance analysis, partial dependence plots, and local interpretable model-agnostic explanations (lime)
– Fairness in machine learning, including strategies for mitigating biases and ensuring equal treatment for all individuals
– Robustness in machine learning, including techniques for detecting and handling adversarial attacks and other forms of model manipulation

Throughout the post, we will provide hands-on examples and real-world case studies to illustrate these concepts in action. By the end of this post, you will have a solid understanding of how to build interpretable, fair, and robust machine learning models using Python.
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