R语言实战机器学习(第二版)

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"Machine Learning with R (PACKT, 2ed, 2015)" 是一本面向数据科学专业人士的指南，旨在帮助读者掌握使用R语言进行机器学习的实用技巧。本书针对最新的库和现代思想进行了更新和升级，适合对机器学习有一定了解但经验不足的读者。书中深入探讨了技术理论，提供了丰富的实践知识，教你如何构建算法和处理数据。通过本书，你将了解到分析复杂数据所需的各种工具，并学会如何根据特定需求选择合适的算法。书中的案例涵盖了常见的机器学习任务，如分类、预测、市场分析和聚类。作者Brett Lantz以解决实际问题的方式引导读者应用机器学习方法，提升数据分析能力。本书的主要章节可能包括： 1. 机器学习基础：介绍机器学习的基本概念，包括监督学习、无监督学习和强化学习。 2. R语言简介：概述R语言环境和数据类型，以及数据预处理的重要性。 3. 数据探索与可视化：利用R中的工具进行数据清洗、转换和可视化，以理解数据的特征和模式。 4. 分类算法：详细讲解逻辑回归、决策树、随机森林等分类算法的工作原理和实现。 5. 回归分析：涵盖线性回归、岭回归、套索回归等方法，用于预测连续变量。 6. 预测与时间序列分析：讨论如何使用ARIMA模型、季节性分解和指数平滑法进行时间序列预测。 7. 聚类分析：介绍K-means、层次聚类等无监督学习方法，用于发现数据的内在结构。 8. 集成学习与模型评估：解释Boosting、Bagging和随机森林等集成技术，以及交叉验证、AUC和ROC曲线等评估指标。 9. 深度学习：探讨R中的深度学习框架，如Keras或H2O，用于处理复杂的数据模式识别任务。 10. 实践项目：提供实际案例研究，展示如何将所学应用于解决现实世界问题。 "Machine Learning with R (2nd Edition)" 是一个全面的教程，它不仅教授理论，还注重实践，帮助读者通过R语言掌握机器学习的核心技能，提升数据驱动决策的能力。无论你是数据科学家、分析师还是对机器学习感兴趣的开发者，这本书都能为你提供宝贵的指导。

Table of Contents

[ vii ]

Random forests 369

Training random forests 370

Evaluating random forest performance 373

Summary 375

Chapter 12: Specialized Machine Learning Topics 377

Working with proprietary les and databases 378

Reading from and writing to Microsoft Excel, SAS, SPSS,

and Stata les 378

Querying data in SQL databases 379

Working with online data and services 381

Downloading the complete text of web pages 382

Scraping data from web pages 383

Parsing XML documents 387

Parsing JSON from web APIs 388

Working with domain-specic data 392

Analyzing bioinformatics data 393

Analyzing and visualizing network data 393

Improving the performance of R 398

Managing very large datasets 398

Generalizing tabular data structures with dplyr 399

Making data frames faster with data.table 401

Creating disk-based data frames with ff 402

Using massive matrices with bigmemory 404

Learning faster with parallel computing 404

Measuring execution time 406

Working in parallel with multicore and snow 406

Taking advantage of parallel with foreach and doParallel 410

Parallel cloud computing with MapReduce and Hadoop 411

GPU computing 412

Deploying optimized learning algorithms 413

Building bigger regression models with biglm 414

Growing bigger and faster random forests with bigrf 414

Training and evaluating models in parallel with caret 414

Summary 416

Index 417

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Preface

[ x ]

Chapter 5, Divide and Conquer – Classication Using Decision Trees and Rules, explores a

couple of learning algorithms whose predictions are not only accurate, but also easily

explained. We'll apply these methods to tasks where transparency is important.

Chapter 6, Forecasting Numeric Data – Regression Methods, introduces machine learning

algorithms used for making numeric predictions. As these techniques are heavily

embedded in the eld of statistics, you will also learn the essential metrics needed to

make sense of numeric relationships.

Chapter 7, Black Box Methods – Neural Networks and Support Vector Machines, covers

two complex but powerful machine learning algorithms. Though the math may

appear intimidating, we will work through examples that illustrate their inner

workings in simple terms.

Chapter 8, Finding Patterns – Market Basket Analysis Using Association Rules, exposes

the algorithm used in the recommendation systems employed by many retailers. If

you've ever wondered how retailers seem to know your purchasing habits better

than you know yourself, this chapter will reveal their secrets.

Chapter 9, Finding Groups of Data – Clustering with k-means, is devoted to a procedure

that locates clusters of related items. We'll utilize this algorithm to identify proles

within an online community.

Chapter 10, Evaluating Model Performance, provides information on measuring

the success of a machine learning project and obtaining a reliable estimate of the

learner's performance on future data.

Chapter 11, Improving Model Performance, reveals the methods employed by the teams

at the top of machine learning competition leaderboards. If you have a competitive

streak, or simply want to get the most out of your data, you'll need to add these

techniques to your repertoire.

Chapter 12, Specialized Machine Learning Topics, explores the frontiers of machine

learning. From working with big data to making R work faster, the topics covered

will help you push the boundaries of what is possible with R.

What you need for this book

The examples in this book were written for and tested with R version 3.2.0 on

Microsoft Windows and Mac OS X, though they are likely to work with any

recent version of R.

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