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.
具体描述
读后感
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...
评分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...
评分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...
评分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...
评分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...
用户评价
适合学习者。
评分一定要看英文版。千万别看中文版，千万别看中文版，千万别看中文版！！几个译者的水平太烂了，高考语文成绩估计不及格。
评分????
评分Birol Emir教授推荐。
评分虽然为此书评分的人并不多，但9分以上的结果是实至名归的，个人甚至认为比《An Introduction to Statistical Learning》还要好，虽然两书都做到了“说人话”这个对非统计专业读者而言很重要的前提，可此书介绍的是中阶难度内容，而非入门，要知道越是高深的东西越是难以“说人话”。此书将最基础、最常用和最重要的模型与算法切开放到回归和分类两大块，解析清楚明了并基于案例，其亮点在于动不动就进行大量模型方法的对比，最终说明了世上根本没有万能的模型范式，好的数据分析需要的是因context制宜、特定领域的专业知识、谨慎细致的洞察力、建模工具本质的理解程度。此外，数据预处理、共线性问题、特征选择是给我印象较深的主题，还有每章最后给出详尽的R代码信息，实用到极致。数据分析进阶必读。
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