Store besparelser
Hurtig levering
Gemte
Log ind
0
Kurv
Bøger
Bestsellers
Kommer snart
Nyheder
Spil og hobby
Hjem
Kategorier
Bestsellers
Kommer snart
Nyheder
FAQ
Konto
Kontakt
Kurv
  1. Bog
  2. Beslutningsteori: generelt
  3. Risikovurdering

Predictive Data Mining Models

Af: Desheng Wu, David L. Olson Engelsk Paperback

Predictive Data Mining Models

Af: Desheng Wu, David L. Olson Engelsk Paperback
Tjek vores konkurrenters priser
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 edition provides 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. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling.  Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting.  

Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.

Vis mere
Tjek vores konkurrenters priser
Normalpris
kr 716
Fragt: 39 kr
6 - 8 hverdage
20 kr
Pakkegebyr
God 4 anmeldelser på
Tjek vores konkurrenters priser
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 edition provides 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. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling.  Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting.  

Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.

Vis mere
Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 125
ISBN-13: 9789811396663
Indbinding: Paperback
Udgave:
ISBN-10: 9811396663
Kategori: Risikovurdering
Udg. Dato: 21 aug 2020
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Verlag, Singapore
Oplagsdato: 21 aug 2020
Forfatter(e): Desheng Wu, David L. Olson
Forfatter(e) Desheng Wu, David L. Olson

Kategori Risikovurdering

ISBN-13 9789811396663

Sprog Engelsk

Indbinding Paperback

Sider 125

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 21 aug 2020

Oplagsdato 21 aug 2020

Forlag Springer Verlag, Singapore

Vi anbefaler også
Rosamunde Pilcher Vejen hjem
Vejen hjem
Rosamunde Pilcher
66 kr
Kategori sammenhænge