封面
版权信息
Contributors
About the authors
About the reviewer
Preface
Who this book is for
What this book covers
What you need for this book
Get in touch
Chapter 1. Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
How machines learn
Machine learning in practice
Machine learning with R
Summary
Chapter 2. Managing and Understanding Data
R data structures
Managing data with R
Exploring and understanding data
Summary
Chapter 3. Lazy Learning – Classification Using Nearest Neighbors
Understanding nearest neighbor classification
Example – diagnosing breast cancer with the k-NN algorithm
Summary
Chapter 4. Probabilistic Learning – Classification Using Naive Bayes
Understanding Naive Bayes
Example – filtering mobile phone spam with the Naive Bayes algorithm
Summary
Chapter 5. Divide and Conquer – Classification Using Decision Trees and Rules
Understanding decision trees
Example – identifying risky bank loans using C5.0 decision trees
Understanding classification rules
Example – identifying poisonous mushrooms with rule learners
Summary
Chapter 6. Forecasting Numeric Data – Regression Methods
Understanding regression
Example – predicting medical expenses using linear regression
Understanding regression trees and model trees
Example – estimating the quality of wines with regression trees and model trees
Summary
Chapter 7. Black Box Methods – Neural Networks and Support Vector Machines
Understanding neural networks
Example – modeling the strength of concrete with ANNs
Understanding support vector machines
Example – performing OCR with SVMs
Summary
Chapter 8. Finding Patterns – Market Basket Analysis Using Association Rules
Understanding association rules
Example – identifying frequently purchased groceries with association rules
Summary
Chapter 9. Finding Groups of Data – Clustering with k-means
Understanding clustering
Finding teen market segments using k-means clustering
Summary
Chapter 10. Evaluating Model Performance
Measuring performance for classification
Estimating future performance
Summary
Chapter 11. Improving Model Performance
Tuning stock models for better performance
Improving model performance with meta-learning
Summary
Chapter 12. Specialized Machine Learning Topics
Managing and preparing real-world data
Working with online data and services
Working with domain-specific data
Improving the performance of R
Summary
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更新时间：2021-03-26 16:14:19
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