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Machine Learning with R
最新章節：
Index
WrittenasatutorialtoexploreandunderstandthepowerofRformachinelearning.Thispracticalguidethatcoversalloftheneedtoknowtopicsinaverysystematicway.Foreachmachinelearningapproach,eachstepintheprocessisdetailed,frompreparingthedataforanalysistoevaluatingtheresults.Thesestepswillbuildtheknowledgeyouneedtoapplythemtoyourowndatasciencetasks.IntendedforthosewhowanttolearnhowtouseR'smachinelearningcapabilitiesandgaininsightfromyourdata.Perhapsyoualreadyknowabitaboutmachinelearning,buthaveneverusedR;orperhapsyouknowalittleRbutarenewtomachinelearning.Ineithercase,thisbookwillgetyouupandrunningquickly.Itwouldbehelpfultohaveabitoffamiliaritywithbasicprogrammingconcepts,butnopriorexperienceisrequired.
目錄(68章)
倒序
- 封面
- 版權信息
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- Chapter 1. Introducing Machine Learning
- The origins of machine learning
- Uses and abuses of machine learning
- How do machines learn?
- Steps to apply machine learning to your data
- Choosing a machine learning algorithm
- Using R for machine learning
- Summary
- Chapter 2. Managing and Understanding Data
- R data structures
- Vectors
- Factors
- Managing data with R
- Exploring and understanding data
- Summary
- Chapter 3. Lazy Learning – Classification Using Nearest Neighbors
- Understanding classification using nearest neighbors
- Diagnosing breast cancer with the kNN 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
- Modeling the strength of concrete with ANNs
- Understanding Support Vector Machines
- 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
- 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
- Working with specialized data
- Improving the performance of R
- Summary
- Index 更新時間：2021-07-23 15:50:03
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