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Machine Learning with R

Brett Lantz 著

更新時間：2021-07-23 15:50:03

開會員，本書免費讀 >

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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Machine Learning with R