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.
具體描述
讀後感
I've read several books in machine learning. • Pattern recognition and machine learning • Introduction of statistical learning • Applied predictive models The first one is a comprehensive book to include all the theories and mathematical formu...
評分I've read several books in machine learning. • Pattern recognition and machine learning • Introduction of statistical learning • Applied predictive models The first one is a comprehensive book to include all the theories and mathematical formu...
評分I've read several books in machine learning. • Pattern recognition and machine learning • Introduction of statistical learning • Applied predictive models The first one is a comprehensive book to include all the theories and mathematical formu...
評分I've read several books in machine learning. • Pattern recognition and machine learning • Introduction of statistical learning • Applied predictive models The first one is a comprehensive book to include all the theories and mathematical formu...
評分I've read several books in machine learning. • Pattern recognition and machine learning • Introduction of statistical learning • Applied predictive models The first one is a comprehensive book to include all the theories and mathematical formu...
用戶評價
適閤學習者。
评分雖然為此書評分的人並不多，但9分以上的結果是實至名歸的，個人甚至認為比《An Introduction to Statistical Learning》還要好，雖然兩書都做到瞭“說人話”這個對非統計專業讀者而言很重要的前提，可此書介紹的是中階難度內容，而非入門，要知道越是高深的東西越是難以“說人話”。此書將最基礎、最常用和最重要的模型與算法切開放到迴歸和分類兩大塊，解析清楚明瞭並基於案例，其亮點在於動不動就進行大量模型方法的對比，最終說明瞭世上根本沒有萬能的模型範式，好的數據分析需要的是因context製宜、特定領域的專業知識、謹慎細緻的洞察力、建模工具本質的理解程度。此外，數據預處理、共綫性問題、特徵選擇是給我印象較深的主題，還有每章最後給齣詳盡的R代碼信息，實用到極緻。數據分析進階必讀。
评分開會見到瞭Max Kuhn，纔想起他這本超級實用手冊
评分開會見到瞭Max Kuhn，纔想起他這本超級實用手冊
评分還不錯 有一些網上看不到的東西 但是也有一些錯誤
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