Predictive Analytics with KNIME
Autor Frank Acitoen Limba Engleză Paperback – 30 noi 2024
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.
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 545.11 lei 38-44 zile | |
| Springer – 30 noi 2024 | 545.11 lei 38-44 zile | |
| Hardback (1) | 706.04 lei 6-8 săpt. | |
| Springer Nature Switzerland – 30 noi 2023 | 706.04 lei 6-8 săpt. |
Preț: 545.11 lei
Preț vechi: 681.38 lei
-20%
Puncte Express: 818
Preț estimativ în valută:
96.40€ • 113.13$ • 83.61£
96.40€ • 113.13$ • 83.61£
Carte tipărită la comandă
Livrare economică 06-12 martie
Specificații
ISBN-13: 9783031456329
ISBN-10: 3031456327
Pagini: 328
Dimensiuni: 155 x 235 x 18 mm
Greutate: 0.5 kg
Editura: Springer
ISBN-10: 3031456327
Pagini: 328
Dimensiuni: 155 x 235 x 18 mm
Greutate: 0.5 kg
Editura: Springer
Cuprins
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
Notă biografică
Frank Acito is Professor emeritus, Indiana University, Bloomington
Textul de pe ultima copertă
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.
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.
Caracteristici
Uses KNIME, which is a comprehensive, open-source software tool for analytics that does not require coding Integrates with R and several examples are used with R nodes, in a KNIME workflow Provides graphic representations of each step taken in analyses, making the analyses self-documenting'/