Decision Trees for Analytics Using SAS Enterprise Miner

Decision Trees for Analytics Using SAS Enterprise Miner PDF

Author: Barry De Ville

Publisher:

Published: 2019-07-03

Total Pages: 268

ISBN-13: 9781642953138

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Decision Trees for Analytics Using SAS Enterprise Miner is the most comprehensive treatment of decision tree theory, use, and applications available in one easy-to-access place. This book illustrates the application and operation of decision trees in business intelligence, data mining, business analytics, prediction, and knowledge discovery. It explains in detail the use of decision trees as a data mining technique and how this technique complements and supplements data mining approaches such as regression, as well as other business intelligence applications that incorporate tabular reports, OLAP, or multidimensional cubes. An expanded and enhanced release of Decision Trees for Business Intelligence and Data Mining Using SAS Enterprise Miner, this book adds up-to-date treatments of boosting and high-performance forest approaches and rule induction. There is a dedicated section on the most recent findings related to bias reduction in variable selection. It provides an exhaustive treatment of the end-to-end process of decision tree construction and the respective considerations and algorithms, and it includes discussions of key issues in decision tree practice. Analysts who have an introductory understanding of data mining and who are looking for a more advanced, in-depth look at the theory and methods of a decision tree approach to business intelligence and data mining will benefit from this book.

Decision Trees for Business Intelligence and Data Mining

Decision Trees for Business Intelligence and Data Mining PDF

Author: Barry De Ville

Publisher: SAS Press

Published: 2006

Total Pages: 224

ISBN-13: 9781590475676

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This example-driven guide illustrates the application and operation of decision trees in data mining, business intelligence, business analytics, prediction, and knowledge discovery. It explains in detail the use of decision trees as a data mining technique and how this technique complements and supplements other business intelligence applications.

Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner

Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner PDF

Author: Olivia Parr-Rud

Publisher: SAS Institute

Published: 2014-10

Total Pages: 182

ISBN-13: 1629593273

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This tutorial for data analysts new to SAS Enterprise Guide and SAS Enterprise Miner provides valuable experience using powerful statistical software to complete the kinds of business analytics common to most industries. This beginnner's guide with clear, illustrated, step-by-step instructions will lead you through examples based on business case studies. You will formulate the business objective, manage the data, and perform analyses that you can use to optimize marketing, risk, and customer relationship management, as well as business processes and human resources. Topics include descriptive analysis, predictive modeling and analytics, customer segmentation, market analysis, share-of-wallet analysis, penetration analysis, and business intelligence. --

Predictive Modeling with SAS Enterprise Miner

Predictive Modeling with SAS Enterprise Miner PDF

Author: Kattamuri S. Sarma

Publisher: SAS Institute

Published: 2017-07-20

Total Pages: 574

ISBN-13: 163526040X

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« Written for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling. »--

Decision Trees With SAS Enterprise Miner

Decision Trees With SAS Enterprise Miner PDF

Author: Scientific Books

Publisher: Createspace Independent Publishing Platform

Published: 2016-01-02

Total Pages: 196

ISBN-13: 9781523218158

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This book shows you how to build decision tree models to predict a categorical target and how to build regression tree models to predict a continuous target. Some examples are presented. One example shows how to build a decision tree model to predict response to direct mail. In this example, the target variable is binary, taking on the values response and no response. Other example shows how to build a regression tree model to forecast a continuous (but interval-scaled) target often used in the auto insurance industry, namely loss frequency . Loss frequency can also be modeled as a categorical target variable if it takes on only a few values but in this example it is treated as a continuous target. Successive chapters present examples that clarify the application of the tree models. The examples are solved step by step with SAS Enterprise Miner in order to make easier the understanding of the methodologies used.

Cluster Analysis and Decision Trees with SAS Enterprise Miner

Cluster Analysis and Decision Trees with SAS Enterprise Miner PDF

Author: Scientific Books

Publisher: CreateSpace

Published: 2015-06-22

Total Pages: 178

ISBN-13: 9781514654477

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SAS Institute implements data mining in Enterprise Miner software, which will be used in this book focused in Cluster Analysis and Decision Trees. SAS Institute defines the concept of Data Mining as the process of selecting (Selecting), explore (Exploring), modify (Modifying), modeling (Modeling) and rating (Assessment) large amounts of data with the aim of uncovering unknown patterns which can be used as a comparative advantage with respect to competitors. This process is summarized with the acronym SEMMA which are the initials of the 5 phases which comprise the process of Data Mining according to SAS Institute."

Applying Predictive Analytics

Applying Predictive Analytics PDF

Author: Richard V. McCarthy

Publisher: Springer

Published: 2019-03-12

Total Pages: 205

ISBN-13: 3030140385

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This textbook presents a practical approach to predictive analytics for classroom learning. It focuses on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life example of how business analytics have been used in various aspects of organizations to solve issue or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes.

Regression Models and Decision Trees with SAS Enterprise Miner

Regression Models and Decision Trees with SAS Enterprise Miner PDF

Author: Scientific Books

Publisher: CreateSpace

Published: 2015-06-22

Total Pages: 188

ISBN-13: 9781514651476

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SAS Institute implements data mining in Enterprise Miner software, which will be used in this book focused in Sampling Tecniques, Exploratory Analysis and Association Rules. SAS Institute defines the concept of Data Mining as the process of selecting (Selecting), explore (Exploring), modify (Modifying), modeling (Modeling) and rating (Assessment) large amounts of data with the aim of uncovering unknown patterns which can be used as a comparative advantage with respect to competitors. This process is summarized with the acronym SEMMA which are the initials of the 5 phases which comprise the process of Data Mining according to SAS Institute.

End-to-End Data Science with SAS

End-to-End Data Science with SAS PDF

Author: James Gearheart

Publisher: SAS Institute

Published: 2020-06-26

Total Pages: 246

ISBN-13: 1642958069

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Learn data science concepts with real-world examples in SAS! End-to-End Data Science with SAS: A Hands-On Programming Guide provides clear and practical explanations of the data science environment, machine learning techniques, and the SAS programming knowledge necessary to develop machine learning models in any industry. The book covers concepts including understanding the business need, creating a modeling data set, linear regression, parametric classification models, and non-parametric classification models. Real-world business examples and example code are used to demonstrate each process step-by-step. Although a significant amount of background information and supporting mathematics are presented, the book is not structured as a textbook, but rather it is a user’s guide for the application of data science and machine learning in a business environment. Readers will learn how to think like a data scientist, wrangle messy data, choose a model, and evaluate the model’s effectiveness. New data scientists or professionals who want more experience with SAS will find this book to be an invaluable reference. Take your data science career to the next level by mastering SAS programming for machine learning models.