共用
发表于 2025-3-21 17:10:16
书目名称Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0319295<br><br> <br><br>书目名称Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0319295<br><br> <br><br>书目名称Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0319295<br><br> <br><br>书目名称Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0319295<br><br> <br><br>书目名称Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0319295<br><br> <br><br>书目名称Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0319295<br><br> <br><br>书目名称Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0319295<br><br> <br><br>书目名称Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0319295<br><br> <br><br>书目名称Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0319295<br><br> <br><br>书目名称Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0319295<br><br> <br><br>
摇曳的微光
发表于 2025-3-21 21:27:06
Uday Kamath,John LiuSingle resource addressing the theory and practice of interpretability and explainability techniques using case studies.Covers exploratory data analysis, feature importance, interpretable algorithms,
Ergots
发表于 2025-3-22 00:30:05
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为现场
发表于 2025-3-22 08:06:06
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准则
发表于 2025-3-22 10:12:44
Exploratory Classification of Time-Series,ore effective models. Since any machine learning model is built from the data, understanding the content on which the model is based is imperative for explainability and interpretability. Many of these techniques that summarize, visualize, and explore data have existed for a long time. There have be
muster
发表于 2025-3-22 14:48:54
Suheir S. Sabbah,Bushra I. Albadawing of how well a model performs from looking at the results of model evaluation is another important way to enhance model explainability. We discuss several techniques to visualize model evaluation including precision-recall curves, ROC curves, residual plots, silhouette coefficients, and others to
muster
发表于 2025-3-22 17:38:44
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脆弱吧
发表于 2025-3-23 00:19:01
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衍生
发表于 2025-3-23 01:34:25
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要塞
发表于 2025-3-23 09:24:25
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