Knight - Mathematical Statistics - 2000 [CRC Press]下载

weixin_39820835 2019-07-08 08:30:15

Knight - Mathematical Statistics - 2000 [CRC Press]
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Knight - Mathematical Statistics - 2000 [CRC Press]

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals. Table of Contents Chapter 1 Machine Learning for Predictive Data Analytics Chapter 2 Data to Insights to Decisions Chapter 3 Data Exploration Chapter 4 Information-based Learning Chapter 5 Similarity-based Learning Chapter 6 Probability-based Learning Chapter 7 Error-based Learning Chapter 8 Evaluation Chapter 9 Case Study: Customer Churn Chapter 10 Case Study: Galaxy Classification Chapter 11 The Art of Machine Learning for Predictive Data Analytics Appendix A Descriptive Statistics and Data Visualization for Machine Learning Appendix B Introduction to Probability for Machine Learning Appendix C Differentiation Techniques for Machine Learning

1.4 EXAMPLE K 不好理解

软件来源微信公众号【学术点滴】无需使用复杂软件也可以运用统计学模型，面向纯小白软件均为一键操作出结果【1】Mathematical Statistics3.2(MS3.2)软件核心功能： (1) 描述性统计 [1] 单列数据分布 [2] 整体数据分布 [3] 正态分布检验 [4] 散点图矩阵 (2) 统计可视化 [5] 柱形图 [6] 树形图 [7] 词云图 [8] 折线图 [9] 散点图 [10] 直方图 [11] 多条折线图 [12] 箱线图 [13] 饼形图 [14] 雷达图 [15] 玫瑰图

Warning: Error in abs: non-numeric argument to mathematical function 解决办法

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