Title
Applied Predictive Modeling,Used
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Winner of the 2014 Technometrics Ziegel Prize for Outstanding BookApplied 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 performanceall of which are problems that occur frequently in practice.The text illustrates all parts of the modeling process through many handson, reallife 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 multipurpose 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.
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- Q: What is the binding type of 'Applied Predictive Modeling'? A: The binding type is hardcover, which ensures durability and a premium feel for readers.
- Q: How many pages does 'Applied Predictive Modeling' have? A: The book contains six hundred thirteen pages, providing comprehensive coverage of predictive modeling techniques.
- Q: What are the dimensions of 'Applied Predictive Modeling'? A: The book measures six point four two inches in length, one point five inches in width, and nine point three inches in height.
- Q: Who is the author of 'Applied Predictive Modeling'? A: The author is Max Kuhn, known for his expertise in predictive analytics and statistical modeling.
- Q: What is the main subject of 'Applied Predictive Modeling'? A: The main subject is predictive modeling, focusing on regression and classification techniques.
- Q: Is 'Applied Predictive Modeling' suitable for beginners? A: Yes, the book is suitable for beginners, as it provides intuitive explanations of predictive modeling concepts.
- Q: Can I use 'Applied Predictive Modeling' for advanced studies? A: Yes, it can be used for advanced undergraduate or graduate-level predictive modeling courses, making it versatile.
- Q: Does 'Applied Predictive Modeling' include practical examples? A: Yes, the text includes numerous hands-on, real-life examples to illustrate modeling concepts.
- Q: What programming language is used in 'Applied Predictive Modeling'? A: The book uses R for its practical examples and includes extensive R code for each modeling step.
- Q: Are problem sets included in 'Applied Predictive Modeling'? A: Yes, each chapter contains problem sets to help solidify the covered concepts for readers.
- Q: How should I store 'Applied Predictive Modeling' to keep it in good condition? A: Store the book in a cool, dry place, away from direct sunlight to preserve its quality.
- Q: Is there a companion R package available for this book? A: Yes, the companion 'AppliedPredictiveModeling' R package is freely available on the CRAN archive.
- Q: What kind of illustrations are in 'Applied Predictive Modeling'? A: The book features black and white illustrations that aid in understanding complex concepts.
- Q: Does 'Applied Predictive Modeling' address model performance issues? A: Yes, it discusses practical concerns like handling class imbalance and identifying causes of poor model performance.
- Q: What makes 'Applied Predictive Modeling' a winner of the Technometrics Ziegel Prize? A: Its comprehensive approach and practical applications in predictive modeling earned it the prestigious award in 2014.