Interpretable Machine Learning with Python

Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable modelsKey Features:Learn how to extract easy-to-understand insights from any machine learning modelBecome well-versed with interpretability techniques to build fairer, safer, and more reliable modelsMitigate risks in AI systems before they have broader implications by learning how to debug black-box modelsBook Description:Do you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models.The first section of the book is a beginners guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. Youll focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, the book also helps the reader to interpret model outcomes using examples. In the third section, youll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods youll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining.By the end of this book, youll be able to understand ML models better and enhance them through interpretability tuning.What You Will Learn:Recognize the importance of interpretability in businessStudy models that are intrinsically interpretable such as linear mo