LIME 5 is a compelling 320-page expose of the American abortion industry written by pro-life activist, Mark Crutcher. A unique & uncensored look at America's most wrenching social issue, it is certain to become required reading for people on both sides of the issue as well as those caught in the middle. Containing 738 endnotes, it documents that women are being sexually assaulted, injured & mutilated inside abortion clinics in numbers that have never before been made public. It also details how pro-choice organizations have used raw political power to fight off regulation of their industry, while simultaneously refusing to self-police. And in one of its most powerful chapters, it exposes a massive cover-up of abortion industry disasters being carried out by an agency of the U.S. Government. Other subjects covered include: how the abortion industry is collapsing because of the abysmal toll abortions take on the people who perform them; the medical evidence that there may be a connection between the rise in America's abortion rate & its parallel rise in breast cancer; the often overwhelming barriers faced by abortion-injured women who seek compensation in the court system; & practical suggestions for solving these problems. Price: $19.95 (US). Contact: Life Dynamics, Incorporated, P.O. Box 2226, Denton, TX 76202, (817) 380-8800, FAX: (817) 380-8700.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.