東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Predictive analytics with KNIME = an...

Acito, Frank.

FindBook

Google Book

Amazon

博客來

Predictive analytics with KNIME = analytics for citizen data scientists /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predictive analytics with KNIME/ by Frank Acito.
其他題名:	analytics for citizen data scientists /
作者:	Acito, Frank.
出版者:	Cham :Springer Nature Switzerland : : 2023.,
面頁冊數:	xiii, 314 p. :illustrations (chiefly color), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Predictive analytics. -
電子資源:	https://doi.org/10.1007/978-3-031-45630-5
ISBN:	9783031456305

Predictive analytics with KNIME = analytics for citizen data scientists /
Acito, Frank.

Predictive analytics with KNIMEanalytics for citizen data scientists /[electronic resource] :by Frank Acito. - Cham :Springer Nature Switzerland :2023. - xiii, 314 p. :illustrations (chiefly color), digital ;24 cm.

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.

ISBN: 9783031456305

Standard No.: 10.1007/978-3-031-45630-5doiSubjects--Uniform Titles:

KNIME (Computer file)
Subjects--Topical Terms:

3487786
Predictive analytics.

LC Class. No.: QA76.9.Q36

Dewey Class. No.: 001.42

Predictive analytics with KNIME = analytics for citizen data scientists /
LDR:02555nmm a2200349 a 4500 001 2336282
003 DE-He213
005 20231129145451.0
006 m d
007 cr nn 008maaau
008 240402s2023 sz s 0 eng d
020 $a 9783031456305 $q (electronic bk.)
020 $a 9783031456299 $q (paper)
024 7 $a 10.1007/978-3-031-45630-5 $2 doi
035 $a 978-3-031-45630-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.Q36
072 7 $a PBT $2 bicssc
072 7 $a K $2 bicssc
072 7 $a BUS061000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a K $2 thema
082 0 4 $a 001.42 $2 23
090 $a QA76.9.Q36 $b A181 2023
100 1 $a Acito, Frank. $3 3669273
245 1 0 $a Predictive analytics with KNIME $h [electronic resource] : $b analytics for citizen data scientists / $c by Frank Acito.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2023.
300 $a xiii, 314 p. : $b illustrations (chiefly color), digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
630 0 0 $a KNIME (Computer file) $3 3669275
650 0 $a Predictive analytics. $3 3487786
650 0 $a Predictive analytics $x Computer programs. $3 3669274
650 1 4 $a Statistics in Business, Management, Economics, Finance, Insurance. $3 3538572
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Statistical Software. $3 3596845
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-45630-5
950 $a Mathematics and Statistics (SpringerNature-11649)