| 书目名称 | Explainable AI Recipes | | 副标题 | Implement Solutions | | 编辑 | Pradeepta Mishra | | 视频video | | | 概述 | Explains the core features of XAI and how to execute them using Python frameworks.Covers interpreting supervised learning algorithms and single instance predictions with XAI.Includes best practices fo | | 图书封面 |  | | 描述 | Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. .The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution,and activation attribution. .After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses..What Y | | 出版日期 | Book 2023 | | 关键词 | Explainable AI; Python; Artificial Intelligence; Linaer Supervised Model; Non Linear Supervised Model; En | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-9029-3 | | isbn_softcover | 978-1-4842-9028-6 | | isbn_ebook | 978-1-4842-9029-3 | | copyright | Pradeepta Mishra 2023 |
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发表于 2025-3-21 16:07:48
