標題: Titlebook: ; [打印本頁] 作者: 美麗動人 時間: 2025-3-21 16:44
書目名稱Guide to Intelligent Data Science影響因子(影響力)
書目名稱Guide to Intelligent Data Science影響因子(影響力)學科排名
書目名稱Guide to Intelligent Data Science網(wǎng)絡公開度
書目名稱Guide to Intelligent Data Science網(wǎng)絡公開度學科排名
書目名稱Guide to Intelligent Data Science被引頻次
書目名稱Guide to Intelligent Data Science被引頻次學科排名
書目名稱Guide to Intelligent Data Science年度引用
書目名稱Guide to Intelligent Data Science年度引用學科排名
書目名稱Guide to Intelligent Data Science讀者反饋
書目名稱Guide to Intelligent Data Science讀者反饋學科排名
作者: Limited 時間: 2025-3-21 21:16 作者: BRIDE 時間: 2025-3-22 03:15
Michael R. Berthold,Christian Borgelt,Rosaria Sili作者: Vldl379 時間: 2025-3-22 07:30 作者: 在前面 時間: 2025-3-22 11:01
https://doi.org/10.1007/978-94-009-5382-6owever, 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.作者: Endemic 時間: 2025-3-22 14:37 作者: Endemic 時間: 2025-3-22 19:26 作者: 可憎 時間: 2025-3-22 21:22
Principles of Modeling,owever, 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.作者: peak-flow 時間: 2025-3-23 01:31
Data Preparation,reater detail (see the following chapters), 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.作者: 不法行為 時間: 2025-3-23 05:34 作者: NIB 時間: 2025-3-23 10:17 作者: nutrition 時間: 2025-3-23 16:28
Finding Explanations,xamples 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.作者: 新鮮 時間: 2025-3-23 20:17 作者: eulogize 時間: 2025-3-24 00:30
Richard S. Ostfeld,Lorrie L. Klostermann overview of clustering methods (hierarchical clustering, .-Means, density-based clustering), association analysis, self-organizing maps and deviation analysis. The definition and choice of distance or similarity measures, which is required by almost every technique to compare different cases in the database, is also tackled.作者: 獎牌 時間: 2025-3-24 03:12 作者: 態(tài)學 時間: 2025-3-24 07:36 作者: Orthodontics 時間: 2025-3-24 14:22
Roberta Capitello,Lucie Sirieixerstanding phase, we know much better whether the assumptions we made during the project understanding phase concerning representativeness, informativeness, data quality, and the presence or absence of external factors are justified.作者: 失望未來 時間: 2025-3-24 17:03
Practical Data Science: An Example,onstrated as well. We will skip algorithmic and other details here and only briefly mention the intention behind applying some of the processes and methods. They will be discussed in depth in subsequent chapters.作者: 閑聊 時間: 2025-3-24 21:45 作者: 銀版照相 時間: 2025-3-24 23:42 作者: 行業(yè) 時間: 2025-3-25 05:57
Practical Data Science: An Example,itfalls one encounters when analyzing real-world data. We start our journey through the data science process by looking over the shoulders of two (pseudo) data scientists, Stan and Laura, working on some hypothetical data science problems in a sales environment. Being differently skilled, they show 作者: 保全 時間: 2025-3-25 08:48 作者: 繼而發(fā)生 時間: 2025-3-25 15:39 作者: 秘方藥 時間: 2025-3-25 17:44
Principles of Modeling,ject understanding phase to revise objectives (or to stop the project). In the former case, we have to prepare the data set for subsequent modeling. However, as some of the data preparation steps are motivated by modeling itself, we first discuss the principles of modeling. Many modeling methods wil作者: Lacerate 時間: 2025-3-25 23:55 作者: Congruous 時間: 2025-3-26 02:53 作者: 圓錐 時間: 2025-3-26 06:06 作者: 強有力 時間: 2025-3-26 11:03 作者: 整頓 時間: 2025-3-26 12:39 作者: phlegm 時間: 2025-3-26 19:20 作者: abduction 時間: 2025-3-26 23:26 作者: 手勢 時間: 2025-3-27 02:47 作者: 諄諄教誨 時間: 2025-3-27 05:54 作者: 胰臟 時間: 2025-3-27 12:48 作者: 返老還童 時間: 2025-3-27 15:42
Social Systems and Learning Systemsations. We intend to apply various modeling techniques to extract models from the data. Although we have not yet discussed any modeling technique in greater detail (see the following chapters), we have already glimpsed at some fundamental techniques and potential pitfalls in the previous chapter. Be作者: 細胞 時間: 2025-3-27 20:08
Richard S. Ostfeld,Lorrie L. Klosterman the identification of areas that exceptionally deviate from the remainder. They provide answers to questions such as: Does it naturally subdivide into groups? How do attributes depend on each other? Are there certain conditions leading to exceptions from the average behavior? The chapter provides a作者: tangle 時間: 2025-3-27 22:32
Reflection, Theory and Language,rder to group similar objects. In this chapter we will discuss methods that address a very different setup: Instead of finding structure in a data set, we are now focusing on methods that find explanations for an unknown dependency within the data. Such a search for a dependency usually focuses on a作者: ingestion 時間: 2025-3-28 05:55
https://doi.org/10.1007/978-3-030-78324-2e discussed methods for basically the same purpose, the methods in this chapter yield models that do not help much to explain the data or even dispense with models altogether. Nevertheless, they can be useful, namely if the main goal is good prediction accuracy rather than an intuitive and interpret作者: prick-test 時間: 2025-3-28 08:29
Testing the Explanatory Value of Naturereted to gain new insights for feature construction (or even data acquisition). What we have ignored so far is the deployment of the models into production as well as their continued monitoring and potentially even automatic updating.作者: 正論 時間: 2025-3-28 12:54
Guide to Intelligent Data Science978-3-030-45574-3Series ISSN 1868-0941 Series E-ISSN 1868-095X 作者: 不來 時間: 2025-3-28 16:30
Testing the Explanatory Value of Naturereted to gain new insights for feature construction (or even data acquisition). What we have ignored so far is the deployment of the models into production as well as their continued monitoring and potentially even automatic updating.作者: motor-unit 時間: 2025-3-28 21:06 作者: 通知 時間: 2025-3-29 01:59 作者: expire 時間: 2025-3-29 04:46
Die heuristische Bedeutung der neuen Lehre,Hintergründen des schizophrenen Krankheitsgeschehens. Sie schafft daher auch neue Zug?nge zum Problem der schizophrenen Gesamtlebensführung, Gewohnheiten, Haltungen, Einstellungen und Verarbeitungsvorg?ngen.作者: 惡名聲 時間: 2025-3-29 10:55
Florian G. Hartmann,Johannes Kopp,Daniel Loisard is being implemented as a stand-alone module and is linked to the SUN Decision Support System (SUNDS). As risk management in SUNDS Tier 2 is quantitatively linked to risk assessment results, organisational risk management—an essential component in addressing complex and uncertain risks that cann作者: blight 時間: 2025-3-29 15:15
Book 20102nd edition-based Radiation Oncology,.?a portable reference that utilizes evidence-based medicine as the basis for practical treatment recommendations and guidelines. Organized by body site, concise clinical chapters provide easy access to critical information. Important "pearls" of epidemiology, anatomy, path
