CYBER DEAL: 50% off all Springer eBooks | Get this offer! Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. JavaScript is currently disabled, this site works much better if you 图书Applied Predictive Modeling 介绍、书评、论坛及推荐 . Altmetric Badge. 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. Among the books, you will fin d those dealing with the mathematical side … zbMATH CrossRef Google Scholar. 39–83. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Kuhn M, Johnson K (2013) Applied predictive modeling, vol 26. Overview of attention for book Table of Contents. With increasing availability of big biomedical data, predictive modeling is increasingly applied to leverage the data for clinical medicine, public health, and biomedical research. It seems that you're in USA. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer Science+Business Media LLC New York. 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. Applied Predictive Modeling is a book on the practice of modeling when accuracy is the primary goal. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. ...you'll find more products in the shopping cart. Applied Predictive Modeling. Chapter 1 Introduction Altmetric Badge. Behnamian A, Millard K, Banks SN, White L, Richardson M, Pasher J (2017) A systematic approach for variable selection with random forests: achieving stable variable importance values. Chapter 2 A Short Tour of the Predictive Modeling Process Altmetric Badge. The list, which includes 408 books in total, covers a wide range of scientific and technological topics.In order to save you some time, I have created one list of all the books (65 in number) that are relevant to the data and Machine Learning field. 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. The text illustrates all parts of … ical treatments of predictive modeling, we suggest Hastie et al. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. Keywords Predictive model Markov blanket Dimensionality … Addressing practical concerns … (gross), © 2020 Springer Nature Switzerland AG. Book Overview. 71. The text then provides intuitive explanations, Over 10 million scientific documents at your fingertips. This service is more advanced with JavaScript available. Springer has released hundreds of free books on a wide range of topics to the general public. Kuhn, Max, Johnson, Kjell. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Hundreds of books are now free to download. “Nonparametric Density Estimation.” In “Nonparametric and Semiparametric Models,” pp. 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. 56 (3), August 2014), “In my judgment, Applied Predictive Modeling by Max Kuhn and Kjell Johnson (Springer 2013) ought to be at the very top of the reading list …They come across like coaches who really, really want you to be able to do this…Applied Predictive Modeling is a remarkable text…it is the succinct distillation of years of experience of two expert modelers…” (Joseph Rickert, blog.revolutionanalytics.com, June 2014), "This strong, technical, hands-on treatment clearly spells out the concepts, and illustrates its themes tangibly with the language R, the most popular open source analytics solution." 1. Springer is part of, Please be advised Covid-19 shipping restrictions apply. Springer has released hundreds of free books on a wide range of topics to the general public. “…In teaching a data science course…I use a range of different resources because I need to cover working with data, model evaluation, and machine learning methods. (2008)and Bishop (2006). price for Spain “Shrinking Trees.” Technical report, AT&T Bell … 85.194.243.237, https://doi.org/10.1007/978-1-4614-6849-3, Springer Science+Business Media New York 2013, A Short Tour of the Predictive Modeling Process, Measuring Performance in Regression Models, Case Study: Compressive Strength of Concrete Mixtures, Measuring Performance in Classification Models, Discriminant Analysis and Other Linear Classification Models, Classification Trees and Rule-Based Models, Factors That Can Affect Model Performance. It guides the reader through many examples that serve to illustrate main points, and it raises possible issues and considerations that are oftentimes overlooked or not sufficiently reflected upon. The data shown below were collected from the profiles of 64 tweeters who shared this research output. 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. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. “In my judgment, Applied Predictive Modeling by Max Kuhn and Kjell Johnson (Springer 2013) ought to be at the very top of the reading list …They come across like coaches who really, really want you to be able to do this…Applied Predictive Modeling is a remarkable text…it is the succinct distillation of years of experience of two expert modelers…” (Joseph Rickert, blog.revolutionanalytics.com, June 2014) In order to save you some time, I have created one list of all the books (65 in number) that are relevant to the data and Machine Learning field. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. His scholarly work centers on the application and development of statistical methodology and learning algorithms. 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. (Eric Siegel, Ph.D. Founder, Predictive Analytics World, Author, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die), A Short Tour of the Predictive Modeling Process, Measuring Performance in Regression Models, Case Study: Compressive Strength of Concrete Mixtures, Measuring Performance in Classification Models, Discriminant Analysis and Other Linear Classification Models, Classification Trees and Rule-Based Models, Factors That Can Affect Model Performance. ... 作者: Max Kuhn / Kjell Johnson 出版社: Springer 出版年: 2013-9-15 页数: 600 定价: USD 89.95 装帧: Hardcover ISBN: 9781461468486. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. (Bojan Tunguz, tunguzreview.com, June 2015), “This monograph presents a very friendly practical course on prediction techniques for regression and classification models… It is a well-written book very useful to students and practitioners who need an immediate and helpful way to apply complex statistical techniques.” (Stan Lipovetsky, Technometrics, Vol. 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. Springer, New York. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. 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. While the text is biased against complex equations, a mathematical background is needed for advanced topics. 豆瓣评 … Not affiliated 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.
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