R语言实战:探索与预测数据的机器学习指南

2星 需积分: 17 49 下载量 111 浏览量 更新于2024-07-19 收藏 323KB PDF 举报
"《机器学习基础:R语言实用指南》是一本面向数据挖掘学生和研究人员的实用教程,特别针对处理大型多变量数据集中的复杂知识探索和预测建模问题。该书的核心内容涵盖了以下几个关键部分: 1. 无监督学习:通过介绍层次聚类、k-means算法、主成分分析(PCA)和对应分析等方法,帮助读者理解和应用这些技术来发现数据集中的潜在结构和模式。 2. 回归分析:讲解线性回归和非线性回归策略,使读者掌握如何预测定量结果值,这对于理解数据中的趋势和关联至关重要。 3. 分类技术:涉及逻辑回归、判别分析、朴素贝叶斯分类器和支持向量机等,让读者学会如何根据定性结果进行预测,提升分类模型的准确性。 4. 高级机器学习:深入探讨k-最近邻法、决策树模型、集成方法(如随机森林和梯度增强)等,这些方法能构建出更强大和稳健的预测模型。 5. 模型选择:涵盖自动选择最佳预测变量组合的方法,如最佳子集选择、逐步回归和正则化回归(如岭回归、lasso和弹性网络)。此外,书中还介绍了基于主成分的回归技术,处理多变量间的高度相关性。 6. 模型验证与评估:提供了一套衡量预测模型性能的工具和技术,确保模型的有效性和可靠性。 7. 模型诊断:讨论如何检测和解决模型中存在的问题,确保模型的稳健和适用性。 本书以R语言为基础,每个章节都包含简明扼要的原理阐述和实际操作示例,便于读者快速上手。最后的R实验室部分,通过系统地实践每章所讨论的方法,进一步巩固理论知识并提升实践技能。无论是对机器学习入门者还是进阶者,这本书都是数据挖掘和预测分析的宝贵参考资料。"
2016-02-25 上传
About This Book Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark Comprehensive practical solutions taking you into the future of machine learning Go a step further and integrate your machine learning projects with Hadoop Who This Book Is For This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. What You Will Learn Implement a wide range of algorithms and techniques for tackling complex data Get to grips with some of the most powerful languages in data science, including R, Python, and Julia Harness the capabilities of Spark and Mahout used in conjunction with Hadoop to manage and process data successfully Apply the appropriate Machine learning technique to address a real-world problem Get acquainted with deep learning and find out how neural networks are being used at the cutting edge of Machine learning Explore the future of Machine learning and dive deeper into polyglot persistence, semantic data, and more Table of Contents Chapter 1. Introduction to Machine learning Chapter 2. Machine learning and Large-scale datasets Chapter 3. An Introduction to Hadoop's Architecture and Ecosystem Chapter 4. Machine Learning Tools, Libraries, and Frameworks Chapter 5. Decision Tree based learning Chapter 6. Instance and Kernel Methods Based Learning Chapter 7. Association Rules based learning Chapter 8. Clustering based learning Chapter 9. Bayesian learning Chapter 10. Regression based learning Chapter 11. Deep learning Chapter 12. Reinforcement learning Chapter 13. Ensemble learning Chapter 14. New generation data architectures for Machine learning