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Titlebook: Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning; Uday Kamath,John Liu Book 2021 The Editor(s) (if a

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21#
發(fā)表于 2025-3-25 06:24:23 | 只看該作者
Suheir S. Sabbah,Bushra I. Albadawiraditional machine learning models used in classification, regression, and clustering. The Pima Indian diabetes dataset is used to perform supervised and unsupervised classification. The insurance claims dataset is used for regression model analysis.
22#
發(fā)表于 2025-3-25 11:09:58 | 只看該作者
23#
發(fā)表于 2025-3-25 11:47:21 | 只看該作者
Introduction to Interpretability and Explainability, us to trust the predictions of real-life applications of AI. Human-understandable explanations will encourage trust and continued adoption of machine learning systems as well as increasing system safety. As an emerging field, explainable AI will be vital for researchers and practitioners in the coming years.
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發(fā)表于 2025-3-25 17:22:06 | 只看該作者
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發(fā)表于 2025-3-25 23:31:20 | 只看該作者
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發(fā)表于 2025-3-26 00:33:27 | 只看該作者
27#
發(fā)表于 2025-3-26 07:30:00 | 只看該作者
28#
發(fā)表于 2025-3-26 11:52:14 | 只看該作者
cognitive science, and visualization, to name a few. This chapter first highlights some of the recent works in research to categorize and analyze the metrics in a common framework. Finally, we give some predictions on the future based on current trajectories, commercial and open-source trends, and innovations in the field.
29#
發(fā)表于 2025-3-26 15:34:48 | 只看該作者
30#
發(fā)表于 2025-3-26 17:27:51 | 只看該作者
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