Interpretable Ai Techniques For Making Machine Learning Models Transparent
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Interpretable Machine Learning
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.
INTERPRETABLE AI: TECHNIQUES FOR MAKING MACHINE LEARNING MODELS TRANSPARENT
Author: Dr. Aadam Quraishi MD
language: en
Publisher: Xoffencerpublication
Release Date: 2024-01-10
The capacity to understand and have trust in the results generated by models is one of the distinguishing characteristics of high-quality scientific research. Because of the significant impact that models and the outcomes of modeling will have on both our work and our personal lives, it is imperative that we have a solid understanding of models and have faith in the results of modeling. This is something that should be kept in mind by analysts, engineers, physicians, researchers, and scientists in general. Many years ago, picking a model that was transparent to human practitioners or customers often meant selecting basic data sources and simpler model forms such as linear models, single decision trees, or business rule systems. This was the case since selecting a model that was transparent required less processing power. This was the situation as a result of the fact that picking a model that was transparent to human practitioners or customers in general entailed picking a model. Even though these more easy approaches were typically the best option, and even though they continue to be the best option today, they are subject to failure in real-world circumstances in which the phenomena being replicated are nonlinear, uncommon or weak, or very distinctive to particular individuals. Despite the fact that they continue to be the best option, they are sensitive to failure in these kinds of scenarios. The conventional trade-off that existed between the precision of prediction models and the simplicity with which they could be interpreted has been abolished; nevertheless, it is likely that this trade-off was never truly required in the first place. There are technologies that are now accessible that can be used to develop modeling systems that are accurate and sophisticated, based on heterogeneous data and techniques for machine learning, and that can also aid human comprehension of and
Interpretable AI
AI doesn't have to be a black box. These practical techniques help shine a light on your model's mysterious inner workings. Make your AI more transparent, and you'll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements. Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You'll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model.