Applied Predictive Modeling (Hardcover)
暫譯: 應用預測模型 (精裝版)

Max Kuhn, Kjell Johnson

  • 出版商: Springer
  • 出版日期: 2013-05-17
  • 售價: $3,600
  • 貴賓價: 9.5$3,420
  • 語言: 英文
  • 頁數: 600
  • 裝訂: Hardcover
  • ISBN: 1461468485
  • ISBN-13: 9781461468486
  • 相關分類: Machine Learning
  • 立即出貨 (庫存 < 3)

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content<p>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.</p><p><b>Dr. Kuhn</b> 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. </p><p><b>Dr. Johnson</b> 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.</p><p><i>Applied Predictive Modeling</i> 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.<br> <br />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.<br> <br>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.<br> <br>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.</p>sourceProduct Description

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本書旨在為廣泛的讀者群提供預測模型的介紹以及應用指南。非數學背景的讀者將會欣賞這些技術的直觀解釋,而強調使用真實數據解決各種應用中的問題將有助於希望擴展專業知識的從業者。讀者應具備基本的統計概念知識,例如相關性和線性回歸分析。雖然本書對複雜方程式有所偏見,但進階主題仍需具備數學背景。

庫恩博士是輝瑞全球研發部門在康乃狄克州格羅頓的非臨床統計主任。他在製藥和診斷行業應用預測模型已有超過15年的經驗,並且是多個R套件的作者。

約翰遜博士在製藥研究與開發領域擁有超過十年的統計諮詢和預測建模經驗。他是Arbor Analytics的共同創辦人,該公司專注於預測建模,並曾擔任輝瑞全球研發部門的統計主任。他的學術工作集中於統計方法學和學習算法的應用與開發。

應用預測建模涵蓋了整體預測建模過程,從數據預處理、數據拆分和模型調整的基礎步驟開始。本書接著提供了許多常見和現代回歸及分類技術的直觀解釋,始終強調說明和解決真實數據問題。解決實際問題的範疇不僅限於模型擬合,還包括處理類別不平衡、選擇預測變數以及確定模型性能不佳的原因——這些都是實務中經常出現的問題。

本書通過許多實際的案例來說明建模過程的各個部分。每一章都包含了每個步驟的廣泛R代碼。數據集和相應的代碼可在本書的伴隨R套件AppliedPredictiveModeling中獲得,該套件可在CRAN存檔中免費獲取。

這本多用途的書籍可以用作預測模型和整體建模過程的入門書、從業者的參考手冊,或作為高級本科或研究生級別的預測建模課程的教材。為此,每一章都包含問題集,以幫助鞏固所涵蓋的概念,並使用本書R套件中的數據。

有興趣實施這些方法的讀者和學生應具備一些基本的R知識。而一些較為進階的主題則需要一定的數學知識。

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