Sas Stat 92 Users Guide Chapter 93 The Varclus Procedure

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Regression Modeling Strategies

Author : Frank E. Harrell
Publisher : Springer Science & Business Media
ISBN 13 : 147573462X
Total Pages : 583 pages
Book Rating : 4.4/5 (757 download)

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Book Synopsis Regression Modeling Strategies by : Frank E. Harrell

Download or read book Regression Modeling Strategies written by Frank E. Harrell and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

Regression Modeling Strategies

Author : Frank E. Harrell , Jr.
Publisher : Springer
ISBN 13 : 3319194259
Total Pages : 598 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Regression Modeling Strategies by : Frank E. Harrell , Jr.

Download or read book Regression Modeling Strategies written by Frank E. Harrell , Jr. and published by Springer. This book was released on 2015-08-14 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modelling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasises problem solving strategies that address the many issues arising when developing multi-variable models using real data and not standard textbook examples. Regression Modelling Strategies presents full-scale case studies of non-trivial data-sets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression. As in the first edition, this text is intended for Masters' or PhD. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modelling techniques.

SAS and R

Author : Ken Kleinman
Publisher : CRC Press
ISBN 13 : 1466584491
Total Pages : 473 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis SAS and R by : Ken Kleinman

Download or read book SAS and R written by Ken Kleinman and published by CRC Press. This book was released on 2014-07-17 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent Tasks The first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition explains how to easily perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and graphics, along with more complex applications. New to the Second Edition This edition now covers RStudio, a powerful and easy-to-use interface for R. It incorporates a number of additional topics, including using application program interfaces (APIs), accessing data through database management systems, using reproducible analysis tools, and statistical analysis with Markov chain Monte Carlo (MCMC) methods and finite mixture models. It also includes extended examples of simulations and many new examples. Enables Easy Mobility between the Two Systems Through the extensive indexing and cross-referencing, users can directly find and implement the material they need. SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Numerous example analyses demonstrate the code in action and facilitate further exploration. The datasets and code are available for download on the book’s website.

Analyzing Linguistic Data

Author : R. H. Baayen
Publisher : Cambridge University Press
ISBN 13 : 1139470736
Total Pages : 40 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Analyzing Linguistic Data by : R. H. Baayen

Download or read book Analyzing Linguistic Data written by R. H. Baayen and published by Cambridge University Press. This book was released on 2008-03-06 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical analysis is a useful skill for linguists and psycholinguists, allowing them to understand the quantitative structure of their data. This textbook provides a straightforward introduction to the statistical analysis of language. Designed for linguists with a non-mathematical background, it clearly introduces the basic principles and methods of statistical analysis, using 'R', the leading computational statistics programme. The reader is guided step-by-step through a range of real data sets, allowing them to analyse acoustic data, construct grammatical trees for a variety of languages, quantify register variation in corpus linguistics, and measure experimental data using state-of-the-art models. The visualization of data plays a key role, both in the initial stages of data exploration and later on when the reader is encouraged to criticize various models. Containing over 40 exercises with model answers, this book will be welcomed by all linguists wishing to learn more about working with and presenting quantitative data.

Statistical Matching

Author : Marcello D'Orazio
Publisher : John Wiley & Sons
ISBN 13 : 0470023546
Total Pages : 268 pages
Book Rating : 4.4/5 (7 download)

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Book Synopsis Statistical Matching by : Marcello D'Orazio

Download or read book Statistical Matching written by Marcello D'Orazio and published by John Wiley & Sons. This book was released on 2006-03-30 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is more statistical data produced in today’s modern society than ever before. This data is analysed and cross-referenced for innumerable reasons. However, many data sets have no shared element and are harder to combine and therefore obtain any meaningful inference from. Statistical matching allows just that; it is the art of combining information from different sources (particularly sample surveys) that contain no common unit. In response to modern influxes of data, it is an area of rapidly growing interest and complexity. Statistical Matching: Theory and Practice introduces the basics of statistical matching, before going on to offer a detailed, up-to-date overview of the methods used and an examination of their practical applications. Presents a unified framework for both theoretical and practical aspects of statistical matching. Provides a detailed description covering all the steps needed to perform statistical matching. Contains a critical overview of the available statistical matching methods. Discusses all the major issues in detail, such as the Conditional Independence Assumption and the assessment of uncertainty. Includes numerous examples and applications, enabling the reader to apply the methods in their own work. Features an appendix detailing algorithms written in the R language. Statistical Matching: Theory and Practice presents a comprehensive exploration of an increasingly important area. Ideal for researchers in national statistics institutes and applied statisticians, it will also prove to be an invaluable text for scientists and researchers from all disciplines engaged in the multivariate analysis of data collected from different sources.

Statistical Pattern Recognition

Author : Andrew R. Webb
Publisher : John Wiley & Sons
ISBN 13 : 0470854782
Total Pages : 516 pages
Book Rating : 4.4/5 (78 download)

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Book Synopsis Statistical Pattern Recognition by : Andrew R. Webb

Download or read book Statistical Pattern Recognition written by Andrew R. Webb and published by John Wiley & Sons. This book was released on 2003-07-25 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition is a very active area of study andresearch, which has seen many advances in recent years. New andemerging applications - such as data mining, web searching,multimedia data retrieval, face recognition, and cursivehandwriting recognition - require robust and efficient patternrecognition techniques. Statistical decision making and estimationare regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fullyupdated with new methods, applications and references. It providesa comprehensive introduction to this vibrant area - with materialdrawn from engineering, statistics, computer science and the socialsciences - and covers many application areas, such as databasedesign, artificial neural networks, and decision supportsystems. * Provides a self-contained introduction to statistical patternrecognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural networks, support vectormachines, and unsupervised classification. * Each section concludes with a description of the applicationsthat have been addressed and with further developments of thetheory. * Includes background material on dissimilarity, parameterestimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' questions tomore lengthy projects. The book is aimed primarily at senior undergraduate and graduatestudents studying statistical pattern recognition, patternprocessing, neural networks, and data mining, in both statisticsand engineering departments. It is also an excellent source ofreference for technical professionals working in advancedinformation development environments. For further information on the techniques and applicationsdiscussed in this book please visit ahref="http://www.statistical-pattern-recognition.net/"www.statistical-pattern-recognition.net/a

Cluster Analysis for Applications

Author : Michael R. Anderberg
Publisher : Academic Press
ISBN 13 : 1483191397
Total Pages : 376 pages
Book Rating : 4.4/5 (831 download)

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Book Synopsis Cluster Analysis for Applications by : Michael R. Anderberg

Download or read book Cluster Analysis for Applications written by Michael R. Anderberg and published by Academic Press. This book was released on 2014-05-10 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis. Comprised of 10 chapters, this book begins with an introduction to the subject of cluster analysis and its uses as well as category sorting problems and the need for cluster analysis algorithms. The next three chapters give a detailed account of variables and association measures, with emphasis on strategies for dealing with problems containing variables of mixed types. Subsequent chapters focus on the central techniques of cluster analysis with particular reference to computational considerations; interpretation of clustering results; and techniques and strategies for making the most effective use of cluster analysis. The final chapter suggests an approach for the evaluation of alternative clustering methods. The presentation is capped with a complete set of implementing computer programs listed in the Appendices to make the use of cluster analysis as painless and free of mechanical error as is possible. This monograph is intended for students and workers who have encountered the notion of cluster analysis.

Credit Risk Scorecards

Author : Naeem Siddiqi
Publisher : John Wiley & Sons
ISBN 13 : 1118429168
Total Pages : 124 pages
Book Rating : 4.1/5 (184 download)

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Book Synopsis Credit Risk Scorecards by : Naeem Siddiqi

Download or read book Credit Risk Scorecards written by Naeem Siddiqi and published by John Wiley & Sons. This book was released on 2012-06-29 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Credit Risk Scorecards "Scorecard development is important to retail financial services in terms of credit risk management, Basel II compliance, and marketing of credit products. Credit Risk Scorecards provides insight into professional practices in different stages of credit scorecard development, such as model building, validation, and implementation. The book should be compulsory reading for modern credit risk managers." —Michael C. S. Wong Associate Professor of Finance, City University of Hong Kong Hong Kong Regional Director, Global Association of Risk Professionals "Siddiqi offers a practical, step-by-step guide for developing and implementing successful credit scorecards. He relays the key steps in an ordered and simple-to-follow fashion. A 'must read' for anyone managing the development of a scorecard." —Jonathan G. Baum Chief Risk Officer, GE Consumer Finance, Europe "A comprehensive guide, not only for scorecard specialists but for all consumer credit professionals. The book provides the A-to-Z of scorecard development, implementation, and monitoring processes. This is an important read for all consumer-lending practitioners." —Satinder Ahluwalia Vice President and Head-Retail Credit, Mashreqbank, UAE "This practical text provides a strong foundation in the technical issues involved in building credit scoring models. This book will become required reading for all those working in this area." —J. Michael Hardin, PhD Professor of StatisticsDepartment of Information Systems, Statistics, and Management ScienceDirector, Institute of Business Intelligence "Mr. Siddiqi has captured the true essence of the credit risk practitioner's primary tool, the predictive scorecard. He has combined both art and science in demonstrating the critical advantages that scorecards achieve when employed in marketing, acquisition, account management, and recoveries. This text should be part of every risk manager's library." —Stephen D. Morris Director, Credit Risk, ING Bank of Canada

Machine Learning for Ecology and Sustainable Natural Resource Management

Author : Grant Humphries
Publisher : Springer
ISBN 13 : 3319969781
Total Pages : 442 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Machine Learning for Ecology and Sustainable Natural Resource Management by : Grant Humphries

Download or read book Machine Learning for Ecology and Sustainable Natural Resource Management written by Grant Humphries and published by Springer. This book was released on 2018-11-05 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.

Skin Aging

Author : Barbara A. Gilchrest
Publisher : Springer Science & Business Media
ISBN 13 : 3540329536
Total Pages : 205 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Skin Aging by : Barbara A. Gilchrest

Download or read book Skin Aging written by Barbara A. Gilchrest and published by Springer Science & Business Media. This book was released on 2006-04-20 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: The topic of skin aging is of growing importance to all working in the field of dermatology, aesthetic medicine and cosmetic medicine. Two internationally well-known and leading experts in the field present a comprehensive state-of-the-art review on all aspects of skin aging. With its clear, concise and reader-friendly format this book has all the potential to become the Bible of skin aging. Every specialist interested in dermatology, aesthetic medicine, cosmetic science, cutaneous biology and aging research will find indispensable information of great value for his or her daily work.

Classification and Multivariate Analysis for Complex Data Structures

Author : Bernard Fichet
Publisher : Springer Science & Business Media
ISBN 13 : 3642133126
Total Pages : 460 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Classification and Multivariate Analysis for Complex Data Structures by : Bernard Fichet

Download or read book Classification and Multivariate Analysis for Complex Data Structures written by Bernard Fichet and published by Springer Science & Business Media. This book was released on 2011-03-04 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growing capabilities in generating and collecting data has risen an urgent need of new techniques and tools in order to analyze, classify and summarize statistical information, as well as to discover and characterize trends, and to automatically bag anomalies. This volume provides the latest advances in data analysis methods for multidimensional data which can present a complex structure: The book offers a selection of papers presented at the first Joint Meeting of the Société Francophone de Classification and the Classification and Data Analysis Group of the Italian Statistical Society. Special attention is paid to new methodological contributions from both the theoretical and the applicative point of views, in the fields of Clustering, Classification, Time Series Analysis, Multidimensional Data Analysis, Knowledge Discovery from Large Datasets, Spatial Statistics.

Handbook of Partial Least Squares

Author : Vincenzo Esposito Vinzi
Publisher : Springer Science & Business Media
ISBN 13 : 3540328270
Total Pages : 791 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Handbook of Partial Least Squares by : Vincenzo Esposito Vinzi

Download or read book Handbook of Partial Least Squares written by Vincenzo Esposito Vinzi and published by Springer Science & Business Media. This book was released on 2010-03-10 with total page 791 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook provides a comprehensive overview of Partial Least Squares (PLS) methods with specific reference to their use in marketing and with a discussion of the directions of current research and perspectives. It covers the broad area of PLS methods, from regression to structural equation modeling applications, software and interpretation of results. The handbook serves both as an introduction for those without prior knowledge of PLS and as a comprehensive reference for researchers and practitioners interested in the most recent advances in PLS methodology.

Finite Mixture Distributions

Author : B. Everitt
Publisher : Springer Science & Business Media
ISBN 13 : 9400958978
Total Pages : 148 pages
Book Rating : 4.4/5 (9 download)

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Book Synopsis Finite Mixture Distributions by : B. Everitt

Download or read book Finite Mixture Distributions written by B. Everitt and published by Springer Science & Business Media. This book was released on 2013-03-08 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finite mixture distributions arise in a variety of applications ranging from the length distribution of fish to the content of DNA in the nuclei of liver cells. The literature surrounding them is large and goes back to the end of the last century when Karl Pearson published his well-known paper on estimating the five parameters in a mixture of two normal distributions. In this text we attempt to review this literature and in addition indicate the practical details of fitting such distributions to sample data. Our hope is that the monograph will be useful to statisticians interested in mixture distributions and to re search workers in other areas applying such distributions to their data. We would like to express our gratitude to Mrs Bertha Lakey for typing the manuscript. Institute oj Psychiatry B. S. Everitt University of London D. l Hand 1980 CHAPTER I General introduction 1. 1 Introduction This monograph is concerned with statistical distributions which can be expressed as superpositions of (usually simpler) component distributions. Such superpositions are termed mixture distributions or compound distributions. For example, the distribution of height in a population of children might be expressed as follows: h(height) = fg(height: age)f(age)d age (1. 1) where g(height: age) is the conditional distribution of height on age, and/(age) is the age distribution of the children in the population.

Finding Groups in Data

Author : Leonard Kaufman
Publisher : Wiley-Interscience
ISBN 13 :
Total Pages : 376 pages
Book Rating : 4.:/5 (318 download)

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Book Synopsis Finding Groups in Data by : Leonard Kaufman

Download or read book Finding Groups in Data written by Leonard Kaufman and published by Wiley-Interscience. This book was released on 1990-03-22 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Partitioning around medoids (Program PAM). Clustering large applications (Program CLARA). Fuzzy analysis (Program FANNY). Agglomerative Nesting (Program AGNES). Divisive analysis (Program DIANA). Monothetic analysis (Program MONA). Appendix.

Statistical Graphics in SAS

Author : Warren F. Kuhfeld
Publisher : Sas Inst
ISBN 13 : 9781607644859
Total Pages : 211 pages
Book Rating : 4.6/5 (448 download)

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Book Synopsis Statistical Graphics in SAS by : Warren F. Kuhfeld

Download or read book Statistical Graphics in SAS written by Warren F. Kuhfeld and published by Sas Inst. This book was released on 2010 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Graph Template Language (GTL) and the Statistical Graphics (SG) procedures are powerful new additions to SAS for creating high-quality statistical graphics. Warren F. Kuhfeld's "Statistical Graphics in SAS: An Introduction to the Graph Template Language and the Statistical Graphics Procedures" provides a parallel and example-driven introduction to the SG procedures and the GTL. Most graphs in the book are produced in at least two ways. Each example provides prototype code for getting started with the GTL and with the SG procedures. While you do not need to write a template to make many useful graphs, understanding the GTL enables you to create custom graphs that cannot be produced by the SG procedures. Knowing the GTL also helps you modify the sometimes complex templates that SAS provides. Written for anyone interested in statistical graphics, Statistical Graphics in SAS is a comprehensive introduction to these two aspects of ODS Graphics. It helps you understand the basics of what you can do with the SG procedures as well as how you can go beyond that by using the full power of the GTL.

Introduction to Spatial Econometrics

Author : James LeSage
Publisher : CRC Press
ISBN 13 : 1420064258
Total Pages : 362 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Introduction to Spatial Econometrics by : James LeSage

Download or read book Introduction to Spatial Econometrics written by James LeSage and published by CRC Press. This book was released on 2009-01-20 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial Econometrics presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observat

Data Mining with R

Author : Luis Torgo
Publisher : CRC Press
ISBN 13 : 1315399091
Total Pages : 426 pages
Book Rating : 4.3/5 (153 download)

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Book Synopsis Data Mining with R by : Luis Torgo

Download or read book Data Mining with R written by Luis Torgo and published by CRC Press. This book was released on 2016-11-30 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining. About the Author Luís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.