Introduction to R in IBM SPSS Modeler PDF Download
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Author: Wannes Rosius
Publisher: IBM Redbooks
ISBN: 0738455601
Category : Computers
Languages : en
Pages : 54
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Book Description
This IBM RedpaperTM publication focuses on the integration between IBM® SPSS® Modeler and R. The paper is aimed at people who know IBM SPSS Modeler and have only a very limited knowledge of R. Chapters 2, 3, and 4 provide you with a high level understanding of R integration within SPSS Modeler enabling you to create or recreate some very basic R models within SPSS Modeler, even if you have only a basic knowledge of R. Chapter 5 provides more detailed tips and tricks. This chapter is for the experienced user and consists of items that might help you get up to speed with more detailed functions of the integration and understand some pitfalls.
Author: Wannes Rosius
Publisher: IBM Redbooks
ISBN: 0738455601
Category : Computers
Languages : en
Pages : 54
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Book Description
This IBM RedpaperTM publication focuses on the integration between IBM® SPSS® Modeler and R. The paper is aimed at people who know IBM SPSS Modeler and have only a very limited knowledge of R. Chapters 2, 3, and 4 provide you with a high level understanding of R integration within SPSS Modeler enabling you to create or recreate some very basic R models within SPSS Modeler, even if you have only a basic knowledge of R. Chapter 5 provides more detailed tips and tricks. This chapter is for the experienced user and consists of items that might help you get up to speed with more detailed functions of the integration and understand some pitfalls.
Author: Pitipong JS Lin
Publisher: IBM Redbooks
ISBN: 0738454141
Category : Computers
Languages : en
Pages : 82
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Book Description
This IBM® RedpaperTM publication presents the process and steps that were taken to move from an R language forecasting solution to an IBM SPSS® Modeler solution. The paper identifies the key challenges that the team faced and the lessons they learned. It describes the journey from analysis through design to key actions that were taken during development to make the conversion successful. The solution approach is described in detail so that you can learn how the team broke the original R solution architecture into logical components in order to plan for the conversion project. You see key aspects of the conversion from R to IBM SPSS Modeler and how basic parts, such as data preparation, verification, pre-screening, and automating data quality checks, are accomplished. The paper consists of three chapters: Chapter 1 introduces the business background and the problem domain. Chapter 2 explains critical technical challenges that the team confronted and solved. Chapter 3 focuses on lessons that were learned during this process and ideas that might apply to your conversion project. This paper applies to various audiences: Decision makers and IT Architects who focus on the architecture, roadmap, software platform, and total cost of ownership. Solution development team members who are involved in creating statistical/analytics-based solutions and who are familiar with R and IBM SPSS Modeler.
Author: Keith McCormick
Publisher: Packt Publishing Ltd
ISBN: 1788296826
Category : Computers
Languages : en
Pages : 231
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Book Description
Get to grips with the fundamentals of data mining and predictive analytics with IBM SPSS Modeler About This Book Get up–and-running with IBM SPSS Modeler without going into too much depth. Identify interesting relationships within your data and build effective data mining and predictive analytics solutions A quick, easy–to-follow guide to give you a fundamental understanding of SPSS Modeler, written by the best in the business Who This Book Is For This book is ideal for those who are new to SPSS Modeler and want to start using it as quickly as possible, without going into too much detail. An understanding of basic data mining concepts will be helpful, to get the best out of the book. What You Will Learn Understand the basics of data mining and familiarize yourself with Modeler's visual programming interface Import data into Modeler and learn how to properly declare metadata Obtain summary statistics and audit the quality of your data Prepare data for modeling by selecting and sorting cases, identifying and removing duplicates, combining data files, and modifying and creating fields Assess simple relationships using various statistical and graphing techniques Get an overview of the different types of models available in Modeler Build a decision tree model and assess its results Score new data and export predictions In Detail IBM SPSS Modeler allows users to quickly and efficiently use predictive analytics and gain insights from your data. With almost 25 years of history, Modeler is the most established and comprehensive Data Mining workbench available. Since it is popular in corporate settings, widely available in university settings, and highly compatible with all the latest technologies, it is the perfect way to start your Data Science and Machine Learning journey. This book takes a detailed, step-by-step approach to introducing data mining using the de facto standard process, CRISP-DM, and Modeler's easy to learn “visual programming” style. You will learn how to read data into Modeler, assess data quality, prepare your data for modeling, find interesting patterns and relationships within your data, and export your predictions. Using a single case study throughout, this intentionally short and focused book sticks to the essentials. The authors have drawn upon their decades of teaching thousands of new users, to choose those aspects of Modeler that you should learn first, so that you get off to a good start using proven best practices. This book provides an overview of various popular data modeling techniques and presents a detailed case study of how to use CHAID, a decision tree model. Assessing a model's performance is as important as building it; this book will also show you how to do that. Finally, you will see how you can score new data and export your predictions. By the end of this book, you will have a firm understanding of the basics of data mining and how to effectively use Modeler to build predictive models. Style and approach This book empowers users to build practical & accurate predictive models quickly and intuitively. With the support of the advanced analytics users can discover hidden patterns and trends.This will help users to understand the factors that influence them, enabling you to take advantage of business opportunities and mitigate risks.
Author: Tilo Wendler
Publisher: Springer Nature
ISBN: 3030543382
Category : Computers
Languages : en
Pages : 1285
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Book Description
Now in its second edition, this textbook introduces readers to the IBM SPSS Modeler and guides them through data mining processes and relevant statistical methods. Focusing on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs, it also features a variety of exercises and solutions, as well as an accompanying website with data sets and SPSS Modeler streams. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice. This revised and updated second edition includes a new chapter on imbalanced data and resampling techniques as well as an extensive case study on the cross-industry standard process for data mining.
Author: Tilo Wendler
Publisher: Springer
ISBN: 9783030543396
Category : Computers
Languages : en
Pages : 0
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Book Description
Now in its second edition, this textbook introduces readers to the IBM SPSS Modeler and guides them through data mining processes and relevant statistical methods. Focusing on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs, it also features a variety of exercises and solutions, as well as an accompanying website with data sets and SPSS Modeler streams. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice. This revised and updated second edition includes a new chapter on imbalanced data and resampling techniques as well as an extensive case study on the cross-industry standard process for data mining.
Author: Howard T. Tokunaga
Publisher: SAGE Publications, Incorporated
ISBN: 1071817043
Category : Psychology
Languages : en
Pages : 313
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Book Description
Are you a researcher or instructor who has been wanting to learn R and RStudio®, but you don′t know where to begin? Do you want to be able to perform all the same functions you use in IBM® SPSS® in R? Is your license to IBM® SPSS® expiring, or are you looking to provide your students guidance to a freely-available statistical software program? Moving from IBM® SPSS® to R and RStudio®: A Statistics Companion is a concise and easy-to-read guide for users who want to know learn how to perform statistical calculations in R. Brief chapters start with a step-by-step introduction to R and RStudio, offering basic installation information and a summary of the differences. Subsequent chapters walk through differences between SPSS and R, in terms of data files, concepts, and structure. Detailed examples provide walk-throughs for different types of data conversions and transformations and their equivalent in R. Helpful and comprehensive appendices provide tables of each statistical transformation in R with its equivalent in SPSS and show what, if any, differences in assumptions factor to into each function. Statistical tests from t-tests to ANOVA through three-factor ANOVA and multiple regression and chi-square are covered in detail, showing each step in the process for both programs. By focusing just on R and eschewing detailed conversations about statistics, this brief guide gives adept SPSS® users just the information they need to transition their data analyses from SPSS to R.
Author: Tilo Wendler
Publisher: Springer
ISBN: 9783319287072
Category : Mathematics
Languages : en
Pages : 0
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Book Description
Introducing the IBM SPSS Modeler, this book guides readers through data mining processes and presents relevant statistical methods. There is a special focus on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs. The variety of exercises and solutions as well as an accompanying website with data sets and SPSS Modeler streams are particularly valuable. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice.
Author: Makenzie Manna
Publisher: IBM Redbooks
ISBN: 0738460923
Category : Computers
Languages : en
Pages : 128
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Book Description
In today's fast-paced, ever-growing digital world, you face various new and complex business problems. To help resolve these problems, enterprises are embedding artificial intelligence (AI) into their mission-critical business processes and applications to help improve operations, optimize performance, personalize the user experience, and differentiate themselves from the competition. Furthermore, the use of AI on the IBM® zSystems platform, where your mission-critical transactions, data, and applications are installed, is a key aspect of modernizing business-critical applications while maintaining strict service-level agreements (SLAs) and security requirements. This colocation of data and AI empowers your enterprise to optimally and easily deploy and infuse AI capabilities into your enterprise workloads with the most recent and relevant data available in real time, which enables a more transparent, accurate, and dependable AI experience. This IBM Redpaper publication introduces and explains AI technologies and hardware optimizations, and demonstrates how to leverage certain capabilities and components to enable AI solutions in business-critical use cases, such as fraud detection and credit risk scoring, on the platform. Real-time inferencing with AI models, a capability that is critical to certain industries and use cases, now can be implemented with optimized performance thanks to innovations like IBM zSystems Integrated Accelerator for AI embedded in the Telum chip within IBM z16TM. This publication describes and demonstrates the implementation and integration of the two end-to-end solutions (fraud detection and credit risk), from developing and training the AI models to deploying the models in an IBM z/OS® V2R5 environment on IBM z16 hardware, and integrating AI functions into an application, for example an IBM z/OS Customer Information Control System (IBM CICS®) application. We describe performance optimization recommendations and considerations when leveraging AI technology on the IBM zSystems platform, including optimizations for micro-batching in IBM Watson® Machine Learning for z/OS. The benefits that are derived from the solutions also are described in detail, including how the open-source AI framework portability of the IBM zSystems platform enables model development and training to be done anywhere, including on IBM zSystems, and enables easy integration to deploy on IBM zSystems for optimal inferencing. Thus, allowing enterprises to uncover insights at the transaction-level while taking advantage of the speed, depth, and securability of the platform. This publication is intended for technical specialists, site reliability engineers, architects, system programmers, and systems engineers. Technologies that are covered include TensorFlow Serving, WMLz, IBM Cloud Pak® for Data (CP4D), IBM z/OS Container Extensions (zCX), IBM CICS, Open Neural Network Exchange (ONNX), and IBM Deep Learning Compiler (zDLC).
Author: Andy Field
Publisher: SAGE
ISBN: 144628915X
Category : Reference
Languages : en
Pages : 994
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Book Description
Keeping the uniquely humorous and self-deprecating style that has made students across the world fall in love with Andy Field′s books, Discovering Statistics Using R takes students on a journey of statistical discovery using R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences throughout the world. The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next you discover the importance of exploring and graphing data, before moving onto statistical tests that are the foundations of the rest of the book (for example correlation and regression). You will then stride confidently into intermediate level analyses such as ANOVA, before ending your journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help you gain the necessary conceptual understanding of what you′re doing, the emphasis is on applying what you learn to playful and real-world examples that should make the experience more fun than you might expect. Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, together with hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more. Given this book′s accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software.
Author: Marvin L.
Publisher:
ISBN: 9781540754837
Category :
Languages : en
Pages : 180
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Book Description
A wide range of applications, such as R, SAS, MATLAB, and SPSS Statistics, provide a huge toolbox of methods to analyze large data and can be used by experts to find patterns and interesting structures in the data. Many of these tools are mainly programming languages, which assumes the analyst has deeper programming skills and an advanced background in IT and mathematics. Since this field is becoming more important, graphic user-interfaced data analysis software is starting to enter the market, providing "drag and drop" mechanisms for career changers and people who are not experts in programming or statistics.One of these easy to handle, data analytics applications is the IBM SPSS Modeler. This book is dedicated to the introduction and explanation of its data analysis power and focused in decision trees. The more important topics are the next: Decision Tree Models General Uses of Tree-Based Analysis C&RT Algorithms CHAID Algorithms QUEST Algorithms C5.0 Algorithms Decision Trees with IM SPSS Modeler Building a Decision Tree with the C5.0 Node Building a decision tree with the CHAID node The C&R Tree node and variable generation The QUEST node-Boosting & Imbalanced data Detection of diabetes-comparison of decision tree nodes Rule set and cross-validation with C5.0 The Auto Classifier Node Building a Stream with the Auto Classifier Node The Auto Classifier Model Nugget Models for credit rating with the Auto Classifier node SVM classifier Interactive decision Trees with IBM SPSS Modeler The Interactive Tree Builder Growing and Pruning the Tree Defining Custom Splits Customizing the Tree View Gains Risks The Growing Directives Generation Filter and Select Nodes Building a Tree Model Directly C&R Tree, CHAID, QUEST, and C 5.0 Models Nuggets Model Nuggets for Boosting, Bagging and Very Large Datasets
