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E-raamat: Just Enough R!: An Interactive Approach to Machine Learning and Analytics [Taylor & Francis e-raamat]

  • Formaat: 346 pages, 33 Tables, black and white; 72 Illustrations, black and white
  • Ilmumisaeg: 08-Jun-2020
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9781003006695
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Just Enough R!: An Interactive Approach to Machine Learning and AnalyticsSuurem pilt
  • Formaat: 346 pages, 33 Tables, black and white; 72 Illustrations, black and white
  • Ilmumisaeg: 08-Jun-2020
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9781003006695
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003006695
"Just Enough R! An Interactive Approach to Machine Learning and Analytics: presents just enough of the R language, machine learning algorithms, statistical methodology, and analytics for the reader to learn how to find interesting structure in data. The approach might be called "seeing then doing" as it first gives step by step explanations using simple, understandable examples of how the various machine learning algorithms work independent of any programming language. This is followed by detailed scripts written in R that apply the algorithms to solve nontrivial problems with real data. The script code is provided allowing the reader to execute the scripts as they study the explanations given in the text"--

Just Enough R! An Interactive Approach to Machine Learning and Analytics presents just enough of the R language, machine learning algorithms, statistical methodology, and analytics for the reader to learn how to find interesting structure in data. The approach might be called "seeing then doing" as it first gives step-by-step explanations using simple, understandable examples of how the various machine learning algorithms work independent of any programming language. This is followed by detailed scripts written in R that apply the algorithms to solve nontrivial problems with real data. The script code is provided, allowing the reader to execute the scripts as they study the explanations given in the text.

Features

  • Gets you quickly using R as a problem-solving tool

  • Uses RStudio’s integrated development environment

  • Shows how to interface R with SQLite

  • Includes examples using R’s Rattle graphical user interface

  • Requires no prior knowledge of R, machine learning, or computer programming

  • Offers over 50 scripts written in R, including several problem-solving templates that, with slight modification, can be used again and again

    • Covers the most popular machine learning techniques, including ensemble-based methods and logistic regression

    • Includes end-of-chapter exercises, many of which can be solved by modifying existing scripts

    • Includes datasets from several areas, including business, health and medicine, and science

    About the Author

    Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato, where he taught and performed research in the Computer and Information Science Department for over 30 years.

    Preface xiii
    Acknowledgment xv
    Author xvii
    Chapter 1 Introduction to Machine Learning
    		1(24)
    1.1 Machine Learning, Statistical Analysis, And Data Science
    		2(1)
    1.2 Machine Learning: A First Example
    		3(3)
    1.2.1 Attribute-Value Format
    		3(1)
    1.2.2 A Decision Tree for Diagnosing Illness
    		4(2)
    1.3 Machine Learning Strategies
    		6(6)
    1.3.1 Classification
    		7(1)
    1.3.2 Estimation
    		7(1)
    1.3.3 Prediction
    		8(3)
    1.3.4 Unsupervised Clustering
    		11(1)
    1.3.5 Market Basket Analysis
    		12(1)
    1.4 Evaluating Performance
    		12(5)
    1.4.1 Evaluating Supervised Models
    		12(1)
    1.4.2 Two-Class Error Analysis
    		13(1)
    1.4.3 Evaluating Numeric Output
    		14(1)
    1.4.4 Comparing Models by Measuring Lift
    		15(2)
    1.4.5 Unsupervised Model Evaluation
    		17(1)
    1.5 Ethical Issues
    		17(1)
    1.6
    Chapter Summary
    		18(1)
    1.7 Key Terms
    		18(2)
    Exercises
    		20(5)
    Chapter 2 Introduction to R
    		25(16)
    2.1 Introducing R And Rstudio
    		25(3)
    2.1.1 Features of R
    		26(1)
    2.1.2 Installing R
    		26(2)
    2.1.3 Installing RStudio
    		28(1)
    2.2 Navigating Rstudio
    		28(10)
    2.2.1 The Console
    		28(2)
    2.2.2 The Source Panel
    		30(2)
    2.2.3 The Global Environment
    		32(5)
    2.2.4 Packages
    		37(1)
    2.3 Where's The Data?
    		38(1)
    2.4 Obtaining Help And Additional Information
    		38(1)
    2.5 Summary
    		39(1)
    Exercises
    		39(2)
    Chapter 3 Data Structures and Manipulation
    		41(20)
    3.1 Data types
    		41(3)
    3.1.1 Character Data and Factors
    		42(2)
    3.2 Single-Mode Data Structures
    		44(3)
    3.2.1 Vectors
    		44(2)
    3.2.2 Matrices and Arrays
    		46(1)
    3.3 Multimode Data Structures
    		47(3)
    3.3.1 Lists
    		47(1)
    3.3.2 Data Frames
    		48(2)
    3.4 Writing Your Own Functions
    		50(8)
    3.4.1 Writing a Simple Function
    		50(2)
    3.4.2 Conditional Statements
    		52(1)
    3.4.3 Iteration
    		53(4)
    3.4.4 Recursive Programming
    		57(1)
    3.5 Summary
    		58(1)
    3.6 Key Terms
    		58(1)
    Exercises
    		59(2)
    Chapter 4 Preparing the Data
    		61(18)
    4.1 A Process Model For Knowledge Discovery
    		61(1)
    4.2 Creating A Target Dataset
    		62(4)
    4.2.1 Interfacing R with the Relational Model
    		64(2)
    4.2.2 Additional Sources for Target Data
    		66(1)
    4.3 Data Preprocessing
    		66(4)
    4.3.1 Noisy Data
    		66(1)
    4.3.2 Preprocessing with R
    		67(2)
    4.3.3 Detecting Outliers
    		69(1)
    4.3.4 Missing Data
    		69(1)
    4.4 Data Transformation
    		70(5)
    4.4.1 Data Normalization
    		70(2)
    4.4.2 Data Type Conversion
    		72(1)
    4.4.3 Attribute and Instance Selection
    		72(2)
    4.4.4 Creating Training and Test Set Data
    		74(1)
    4.4.5 Cross Validation and Bootstrapping
    		74(1)
    4.4.6 Large-Sized Data
    		75(1)
    4.5
    Chapter Summary
    		75(1)
    4.6 Key Terms
    		76(1)
    Exercises
    		77(2)
    Chapter 5 Supervised Statistical Techniques
    		79(48)
    5.1 Simple Linear Regression
    		79(6)
    5.2 Multiple Linear Regression
    		85(14)
    5.2.1 Multiple Linear Regression: An Example
    		85(3)
    5.2.2 Evaluating Numeric Output
    		88(1)
    5.2.3 Training/Test Set Evaluation
    		89(2)
    5.2.4 Using Cross Validation
    		91(2)
    5.2.5 Linear Regression with Categorical Data
    		93(6)
    5.3 Logistic Regression
    		99(10)
    5.3.1 Transforming the Linear Regression Model
    		100(1)
    5.3.2 The Logistic Regression Model
    		100(1)
    5.3.3 Logistic Regression with R
    		101(3)
    5.3.4 Creating a Confusion Matrix
    		104(1)
    5.3.5 Receiver Operating Characteristics (ROC) Curves
    		104(4)
    5.3.6 The Area under an ROC Curve
    		108(1)
    5.4 Naive Bayes Classifier
    		109(4)
    5.4.1 Bayes Classifier: An Example
    		109(3)
    5.4.2 Zero-Valued Attribute Counts
    		112(1)
    5.4.3 Missing Data
    		112(1)
    5.44 Numeric Data
    		113(7)
    5.4.5 Experimenting with Naive Bayes
    		115(5)
    5.5
    Chapter Summary
    		120(1)
    5.6 Key Terms
    		120(2)
    Exercises
    		122(5)
    Chapter 6 Tree-Based Methods
    		127(34)
    6.1 A Decision Tree Algorithm
    		127(6)
    6.1.1 An Algorithm for Building Decision Trees
    		128(1)
    6.1.2 C4.5 Attribute Selection
    		128(5)
    6.1.3 Other Methods for Building Decision Trees
    		133(1)
    6.2 Building Decision Trees: C5.0
    		133(4)
    6.2.1 A Decision Tree for Credit Card Promotions
    		134(1)
    6.2.2 Data for Simulating Customer Churn
    		135(1)
    6.2.3 Predicting Customer Churn with C5.0
    		136(1)
    6.3 Building Decision Trees: R Part
    		137(10)
    6.3.1 An rpart Decision Tree for Credit Card Promotions
    		139(2)
    6.3.2 Train and Test rpart: Churn Data
    		141(2)
    6.3.3 Cross Validation rpart: Churn Data
    		143(4)
    6.4 Building Decision Trees: J48
    		147(2)
    6.5 Ensemble Techniques For Improving Performance
    		149(5)
    6.5.1 Bagging
    		149(1)
    6.5.2 Boosting
    		150(1)
    6.5.3 Boosting: An Example with C5.0
    		150(1)
    6.5.4 Random Forests
    		151(3)
    6.6 Regression Trees
    		154(2)
    6.7
    Chapter Summary
    		156(1)
    6.8 Key Terms
    		157(1)
    Exercises
    		157(4)
    Chapter 7 Rule-Based Techniques
    		161(28)
    7.1 From trees to rules
    		161(4)
    7.1.1 The Spam Email Dataset
    		162(1)
    7.1.2 Spam Email Classification: C5.0
    		163(2)
    7.2 A Basic Covering Rule Algorithm
    		165(4)
    7.2.1 Generating Covering Rules with JRip
    		166(3)
    7.3 Generating Association Rules
    		169(8)
    7.3.1 Confidence and Support
    		169(1)
    7.3.2 Mining Association Rules: An Example
    		170(3)
    7.3.3 General Considerations
    		173(1)
    7.3.4 Rweka's Apriori Function
    		173(4)
    7.4 Shake, Rattle, And Roll
    		177(7)
    7.5
    Chapter Summary
    		184(1)
    7.6 Key Terms
    		184(1)
    Exercises
    		185(4)
    Chapter 8 Neural Networks
    		189(50)
    8.1 Feed-Forward Neural Networks
    		190(4)
    8.1.1 Neural Network Input Format
    		190(2)
    8.1.2 Neural Network Output Format
    		192(1)
    8.1.3 The Sigmoid Evaluation Function
    		193(1)
    8.2 Neural Network Training: A Conceptual View
    		194(2)
    8.2.1 Supervised Learning with Feed-Forward Networks
    		194(1)
    8.2.2 Unsupervised Clustering with Self-Organizing Maps
    		195(1)
    8.3 Neural Network Explanation
    		196(1)
    8.4 General Considerations
    		197(1)
    8.4.1 Strengths
    		197(1)
    8.4.2 Weaknesses
    		198(1)
    8.5 Neural Network Training: A Detailed View
    		198(5)
    8.5.1 The Backpropagation Algorithm: An Example
    		198(4)
    8.5.2 Kohonen Self-Organizing Maps: An Example
    		202(1)
    8.6 Building Neural Networks With R
    		203(20)
    8.6.1 The Exclusive-OR Function
    		204(2)
    8.6.2 Modeling Exclusive-OR with MLP: Numeric Output
    		206(4)
    8.6.3 Modeling Exclusive-OR with MLP: Categorical Output
    		210(2)
    8.6.4 Modeling Exclusive-OR with neuralnet: Numeric Output
    		212(2)
    8.6.5 Modeling Exclusive-OR with neuralnet: Categorical Output
    		214(2)
    8.6.6 Classifying Satellite Image Data
    		216(4)
    8.6.7 Testing for Diabetes
    		220(3)
    8.7 Neural Net Clustering For Attribute Evaluation
    		223(4)
    8.8 Times Series Analysis
    		227(5)
    8.8.1 Stock Market Analytics
    		227(1)
    8.8.2 Time Series Analysis: An Example
    		228(1)
    8.8.3 The Target Data
    		229(1)
    8.8.4 Modeling the Time Series
    		230(2)
    8.8.5 General Considerations
    		232(1)
    8.9
    Chapter Summary
    		232(1)
    8.10 Key Terms
    		233(1)
    Exercises
    		234(5)
    Chapter 9 Formal Evaluation Techniques
    		239(18)
    9.1 What Should Be Evaluated?
    		240(1)
    9.2 Tools For Evaluation
    		241(6)
    9.2.1 Single-Valued Summary Statistics
    		242(1)
    9.2.2 The Normal Distribution
    		242(2)
    9.2.3 Normal Distributions and Sample Means
    		244(1)
    9.2.4 A Classical Model for Hypothesis Testing
    		245(2)
    9.3 Computing Test Set Confidence Intervals
    		247(2)
    9.4 Comparing Supervised Models
    		249(4)
    9.4.1 Comparing the Performance of Two Models
    		251(1)
    9.4.2 Comparing the Performance of Two or More Models
    		252(1)
    9.5 Confidence Intervals For Numeric Output
    		253(1)
    9.6
    Chapter Summary
    		253(1)
    9.7 Key Terms
    		254(1)
    Exercises
    		255(2)
    Chapter 10 Support Vector Machines
    		257(22)
    10.1 Linearly Separable Classes
    		259(5)
    10.2 The Nonlinear Case
    		264(1)
    10.3 Experimenting With Linearly Separable Data
    		265(2)
    10.4 Microarray Data Mining
    		267(2)
    10.4.1 DNA and Gene Expression
    		267(1)
    10.4.2 Preprocessing Microarray Data: Attribute Selection
    		268(1)
    10.4.3 Microarray Data Mining: Issues
    		269(1)
    10.5 A Microarray Application
    		269(5)
    10.5.1 Establishing a Benchmark
    		270(1)
    10.5.2 Attribute Elimination
    		271(3)
    10.6
    Chapter Summary
    		274(1)
    10.7 Key Terms
    		275(1)
    Exercises
    		275(4)
    Chapter 11 Unsupervised Clustering Techniques
    		279(32)
    11.1 The K-Means Algorithm
    		280(4)
    11.1.1 An Example Using K-Means
    		280(3)
    11.1.2 General Considerations
    		283(1)
    11.2 Agglomerative Clustering
    		284(3)
    11.2.1 Agglomerative Clustering: An Example
    		284(2)
    11.2.2 General Considerations
    		286(1)
    11.3 Conceptual Clustering
    		287(4)
    11.3.1 Measuring Category Utility
    		287(1)
    11.3.2 Conceptual Clustering: An Example
    		288(2)
    11.3.3 General Considerations
    		290(1)
    11.4 Expectation Maximization
    		291(1)
    11.5 Unsupervised Clustering With R
    		292(13)
    11.5.1 Supervised Learning for Cluster Evaluation
    		292(2)
    11.5.2 Unsupervised Clustering for Attribute Evaluation
    		294(3)
    11.5.3 Agglomerative Clustering: A Simple Example
    		297(1)
    11.5.4 Agglomerative Clustering of Gamma-Ray Burst Data
    		298(3)
    11.5.5 Agglomerative Clustering of Cardiology Patient Data
    		301(2)
    11.5.6 Agglomerative Clustering of Credit Screening Data
    		303(2)
    11.6
    Chapter Summary
    		305(1)
    11.7 Key Terms
    		306(1)
    Exercises
    		307(4)
    Chapter 12 A Case Study in Predicting Treatment Outcome
    		311(10)
    12.1 Goal Identification
    		313(1)
    12.2 A Measure Of Treatment Success
    		314(1)
    12.3 Target Data Creation
    		315(1)
    12.4 Data Preprocessing
    		316(1)
    12.5 Data Transformation
    		316(1)
    12.6 Data Mining
    		316(2)
    12.6.1 Two-Class Experiments
    		316(2)
    12.7 Interpretation And Evaluation
    		318(1)
    12.7.1 Should Patients Torso Rotate?
    		318(1)
    12.8 Taking Action
    		319(1)
    12.9
    Chapter Summary
    		319(2)
    Bibliography 321(6)
    Appendix A Supplementary Materials And More Datasets 327(2)
    Appendix B Statistics For Performance Evaluation 329(6)
    Subject Index 335(6)
    Index Of R Functions 341(2)
    Script Index 343
    Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato where he taught and performed research in the Computer & Information Science Department for 27 years. Dr. Roigers Ph.D. degree is in Computer & Information Sciences from the University of Minnesota. Dr. Roiger continues to serve as a part-time faculty member teaching courses in data mining, artificial intelligence and research methods. Richard enjoys interacting with his grandchildren, traveling, writing and pursuing his musical talents.
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