| 書目名稱 | Predictive Data Mining Models |
| 編輯 | David L. Olson,Desheng Wu |
| 視頻video | http://file.papertrans.cn/755/754585/754585.mp4 |
| 概述 | Provides a comprehensive overview of knowledge management, big data, and basic descriptive data mining methods and software.Illustrates concepts with typical data.Demonstrates readily available open s |
| 叢書名稱 | Computational Risk Management |
| 圖書封面 |  |
| 描述 | This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R’) and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems.? Data mining includes descriptive and predictive modeling.? Operations research includes all three. This book focuses on prescriptive analytics..The book seeks to provide simple explanations and demonstration of some descriptive tools. This second editionprovides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chap |
| 出版日期 | Book 2020Latest edition |
| 關(guān)鍵詞 | Prescriptive data mining; Logistic regression; Decision trees; Forecasting; Neural networks |
| 版次 | 2 |
| doi | https://doi.org/10.1007/978-981-13-9664-9 |
| isbn_softcover | 978-981-13-9666-3 |
| isbn_ebook | 978-981-13-9664-9Series ISSN 2191-1436 Series E-ISSN 2191-1444 |
| issn_series | 2191-1436 |
| copyright | Springer Nature Singapore Pte Ltd. 2020 |
|Archiver|手機(jī)版|小黑屋|
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