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＜電子ブック＞
Predictive Analytics with KNIME : Analytics for Citizen Data Scientists / by Frank Acito

版	1st ed. 2023.
出版者	(Cham : Springer Nature Switzerland : Imprint: Springer)
出版年	2023
本文言語	英語
大きさ	XIII, 314 p. 155 illus., 130 illus. in color : online resource
著者標目	*Acito, Frank author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Data mining LCSH:Statistics -- Computer programs  全ての件名で検索 FREE:Statistics in Business, Management, Economics, Finance, Insurance FREE:Data Mining and Knowledge Discovery FREE:Statistical Software
一般注記	Chapter 1 Introduction to analytics -- Chapter 2 Problem definition -- Chapter 3 Introduction to KNIME -- Chapter 4 Data preparation -- Chapter 5 Dimensionality reduction and feature extraction -- Chapter 6 Ordinary least squares regression -- Chapter 7 Logistic regression -- Chapter 8 Decision and regression trees -- Chapter 9 Naïve Bayes -- Chapter 10 k nearest neighbors -- Chapter 11 Neural networks -- Chapter 12 Ensemble models -- Chapter 13 Cluster analysis -- Chapter 14 Communication and deployment This book is about data analytics, including problem definition, data preparation, and data analysis. A variety of techniques (e.g., regression, logistic regression, cluster analysis, neural nets, decision trees, and others) are covered with conceptual background as well as demonstrations of KNIME using each tool. The book uses KNIME, which is a comprehensive, open-source software tool for analytics that does not require coding but instead uses an intuitive drag-and-drop workflow to create a network of connected nodes on an interactive canvas. KNIME workflows provide graphic representations of each step taken in analyses, making the analyses self-documenting. The graphical documentation makes it easy to reproduce analyses, as well as to communicate methods and results to others. Integration with R is also available in KNIME, and several examples using R nodes in a KNIME workflow are demonstrated for special functions and tools not explicitly included in KNIME HTTP:URL=https://doi.org/10.1007/978-3-031-45630-5

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