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Michael C. Feathers
The average book on Agile software development describes a fairyland of greenfield projects, with wall-to-wall tests that run after every few edits, and clean & simple source code.
The average software project, in our industry, was written under some aspect of code-and-fix, and without automated unit tests. And we can't just throw this code away; it represents a significant effort debugging and maintaining. It contains many latent requirements decisions. Just as Agile processes are incremental, Agile adoption must be incremental too. No more throwing away code just because it looked at us funny.
Mike begins his book with a very diplomatic definition of "Legacy". I'l skip ahead to the undiplomatic version: Legacy code is code without unit tests.
Before cleaning that code up, and before adding new features and removing bugs, such code must be de-legacified. It needs unit tests.
To add unit tests, you must change the code. To change the code, you need unit tests to show how safe your change was.
The core of the book is a cookbook of recipes to conduct various careful attacks. Each presents a particular problem, and a relatively safe way to migrate the code towards tests.
Code undergoing this migration will begin to experience the benefits of unit tests, and these benefits will incrementally make new tests easier to write. These efforts will make aspects of a legacy codebase easy to change.
It's an unfortunate commentary on the state of our programming industry how much we need this book.
Christopher M. Bishop
The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a grounding in the basic concepts of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners.
Max Kuhn, Kjell Johnson
This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance—all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code for each step of the process. The data sets and corresponding code are available in the book’s companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. Readers and students interested in implementing the methods should have some basic knowledge of R. And a handful of the more advanced topics require some mathematical knowledge.
Trevor Hastie, Robert Tibshirani, Jerome H. Friedman
This book describes the important ideas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry.
This book is designed for self study. The reader can apply the theoretical concepts directly within R by following the examples.
'In this brilliant new edition Andy Field has introduced important new introductory material on statistics that the student will need and was missing at least in the first edition. This book is the best blend that I know of a textbook in statistics and a manual on SPSS. It is a balanced composite of both topics, using SPSS to illustrate important statistical material and, through graphics, to make visible important approaches to data analysis. There are many places in the book where I had to laugh, and that's saying a lot for a book on statistics. His excellent style engages the reader and makes reading about statistics fun' - David C Howell, Professor Emeritus, University of Vermont USA This award-winning text, now fully updated with SPSS Statistics, is the only book on statistics that you will need! Fully revised and restructured, this new edition is even more accessible as it now takes students through from introductory to advanced level concepts, all the while grounding knowledge through the use of SPSS Statistics. Andy Field's humorous and self-deprecating style and the book's host of characters make the journey entertaining as well as educational. While still providing a very comprehensive collection of statistical methods, tests and procedures, and packed with examples and self-assessment tests to reinforce knowledge, the new edition now also offers: - a more gentle introduction to basic-level concepts and methods for beginners - new textbook features to make the book more user-friendly for those learning about more advanced concepts, encouraging 'critical thinking' - a brand new, full-colour design, making it easy for students to navigate between topics, and to understand how to use the latest version of SPSS Statistics - both 'real world' (the bizarre and the wonderful) and invented examples illustrate the concepts and make the techniques come alive for students - an additional chapter on multilevel modelling for advanced-level students - reinforced binding to make the book easier to handle at a computer workstation. The book also includes access to a brand new and improved companion Website, bursting with features including: - animated 'SPSS walk-through' videos clearly demonstrating how to use the latest SPSS Statistics modules - self-marking multiple choice questions - data sets for psychology, business and management and health sciences - a flash-card glossary for testing knowledge of key concepts - access to support material from SAGE study skills books. Statistics lecturers are also provided with a whole range of resources and teaching aids, including: - the test bank - over 300 multiple-choice questions ready to upload to WebCT, Blackboard or other virtual learning environments - charts and diagrams in electronic format for inclusion in lecture slides - PowerPoint slides written by the author to accompany chapters of the text.