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发表于 2025-3-26 23:17:56 | 显示全部楼层
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Evaluation and Deployment,tackle these two steps only briefly in the following two sections. A deployment in the form of, say, a software system for decision support involves several planning and coordination tasks, which are out of the scope of this book.
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My Life in the L-café from Different Angles Examples for such problems would be understanding why a customer belongs to the category of people who cancel their account (e.g., classifying her into a yes/no category) or better understanding the risk factors of customers in general.
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https://doi.org/10.1007/978-3-031-52399-1 to the application domain, the models they yield are basically “black boxes” and almost impossible to interpret in terms of the application domain. Hence they should be considered only if a comprehensible model that can easily be checked for plausibility is not required, and high accuracy is the main concern.
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Finding Explanations, Examples for such problems would be understanding why a customer belongs to the category of people who cancel their account (e.g., classifying her into a yes/no category) or better understanding the risk factors of customers in general.
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The French: A Cross-cultural Comparison,wever, as some of the data preparation steps are motivated by modeling itself, we first discuss the principles of modeling. Many modeling methods will be introduced in the following chapters, but this chapter is devoted to problems and aspects that are inherent in and common to all the methods for analyzing the data.
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Mark Burton,Carolyn Kagan,Pat Clementsreater detail (see Chaps. 7ff), we have already glimpsed at some fundamental techniques and potential pitfalls in the previous chapter. Before we start modeling, we have to prepare our data set appropriately, that is, we are going to modify our dataset so that the modeling techniques are best supported but least biased.
发表于 2025-3-28 06:46:31 | 显示全部楼层
Guide to Intelligent Data Analysis978-1-84882-260-3Series ISSN 1868-0941 Series E-ISSN 1868-095X
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