Applied Predictive Modeling (Hardcover) (應用預測建模)
Max Kuhn, Kjell Johnson
- 出版商: Springer
- 出版日期: 2013-05-17
- 定價: $3,500
- 售價: 9.0 折 $3,150
- 語言: 英文
- 頁數: 600
- 裝訂: Hardcover
- ISBN: 1461468485
- ISBN-13: 9781461468486
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相關分類:
Machine Learning
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商品描述
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
商品描述(中文翻譯)
這本書的內容旨在為廣大讀者提供預測模型的介紹和應用指南。非數學背景的讀者將會喜歡這些技術的直觀解釋,而強調實際數據在各種應用中的問題解決能力將有助於希望擴展專業知識的從業人員。讀者應該具備基本的統計概念知識,如相關性和線性回歸分析。雖然本書偏向避免複雜的方程式,但高級主題需要數學背景。
Dr. Kuhn是康涅狄格州格羅頓的輝瑞全球研發部門的非臨床統計主管。他在制藥和診斷行業應用預測模型已有15年以上的經驗,並且是多個R軟體包的作者。
Dr. Johnson在制藥研究和開發領域擁有十多年的統計諮詢和預測建模經驗。他是Arbor Analytics的聯合創始人,該公司專注於預測建模,並曾擔任輝瑞全球研發部門統計主管。他的學術工作集中在統計方法和學習算法的應用和發展。
《應用預測建模》涵蓋了整個預測建模過程,從關鍵的數據預處理、數據分割和模型調整的基礎開始。然後,本書以直觀的方式解釋了許多常見和現代的回歸和分類技術,始終強調通過實際數據問題的示例和解決方法。解決實際問題的實用考慮超出了模型擬合,還包括處理類別不平衡、選擇預測變量和找出模型性能不佳原因等問題,這些問題在實踐中經常出現。
本書通過許多實際案例展示了建模過程的各個部分。每一章節都包含了該過程的廣泛R代碼。數據集和相應的代碼可以在本書的配套R軟體包AppliedPredictiveModeling中找到,該軟體包可以在CRAN存檔中免費獲得。
這本多功能的書可以作為預測模型和整個建模過程的入門書,從業人員的參考手冊,或高級本科或研究生級別的預測建模課程教材。為此,每一章節都包含問題集,以幫助鞏固所涵蓋的概念,並使用本書的R軟體包中提供的數據。
對於有興趣實施這些方法的讀者和學生,需要一些基本的R知識,而一些更高級的主題則需要一些數學知識。