دانلود کتاب پیش‌بینی انطباقی (Conformal Prediction) کاربردی با پایتون: ساخت هوش مصنوعی قابل‌اتکا و مورد اعتماد با استفاده از تعیین کمیت عدم‌قطعیت و پیش‌بینی

This book is an ultimate practical guide to understanding and implementing conformal prediction for building trustworthy, uncertainty-aware machine learning systems. The book introduces the fundamental concepts of conformal prediction, a model-agnostic, distribution-free framework for quantifying predictive uncertainty and demonstrates their practical application through clear explanations and hands-on Python examples based on real-world problems using illustrative datasets. Covering both the theoretical foundations and practical implementation, it enables readers to bridge the gap between predictive accuracy and reliability. Hence, whether you are a data scientist, machine learning engineer, researcher, or student, this book provides the knowledge and tools to develop AI systems that are not only accurate but also transparent, robust, and dependable. By fostering a deeper understanding of predictive uncertainty and model reliability, the concepts presented in this book are applicable across a wide range of machine learning tasks, from simple predictive models to complex, large-scale AI systems, making conformal prediction an essential component of every machine learning practitioner’s toolkit. This book is organized into seven chapters, beginning with the Python environment and code setup required for all experiments. It then introduces the application of conformal prediction across major machine learning tasks, including regression, classification, and forecasting, before exploring techniques for scaling these methods, and deploying them in real-world scenarios across diverse domains such as finance and retail. Throughout the book, all the concepts are explained in a clear and intuitive manner using visualizations, real-world examples, hands-on Python exercises, and chapter-end quizzes to reinforce learning, and keep readers engaged. The following is a brief chapter-wise summary: Chapter 1 introduces the motivation behind Conformal Prediction (CP) by addressing the fundamental issue of uncertainty in machine learning. It explains why traditional models, while accurate, fail to express confidence in their predictions, and how this limitation undermines decision reliability. Readers will learn the concept of distribution-free, model-agnostic uncertainty estimation, and understand why Conformal Prediction offers a critical step toward building trustworthy AI systems. Chapter 2 builds the theoretical base for Conformal Prediction, covering its statistical principles, calibration methods, and key assumptions. It introduces conformity scores, exchangeability, and coverage concepts – the mathematical building blocks that make CP both valid and modelindependent. Readers also set up their Python environment and get familiar with the essential CP libraries. Chapter 3 explores how Conformal Prediction extends to classification problems, transforming single-label outputs into calibrated prediction sets. It walks through Inductive, Mondrian, and Adaptive CP methods with detailed Python implementations, helping readers handle imbalanced data and evolving distributions. Chapter 4 introduces Conformal Prediction for continuous targets, focusing on constructing prediction intervals that maintain statistical validity. It explains Split, Cross, Jackknife+, and Quantile-based approaches, with exercises using real datasets. Readers learn how to balance interval width with coverage to create interpretable and efficient models. Chapter 5 adapts Conformal Prediction to time-series forecasting, where the exchangeability assumption breaks due to temporal order. It covers sliding-window, weighted and adaptive approaches to maintain valid uncertainty estimates over time, with applications in energy and demand forecasting. Chapter 6 focuses on computational scalability in Conformal Prediction, detailing techniques to handle large, high-dimensional, or streaming datasets. Readers learn methods like batch calibration, approximate quantile estimation, and distributed computation for real-world deployment. Chapter 7 , the final chapter wraps everything together, and connects Conformal Prediction to modern AI applications, showcasing its role in different domains like finance, retail, and so on through end to end case studies.