Just Enough R An Interactive Approach to Machine Learning and Analytics
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...
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| Sprache: | Englisch |
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CRC Press
2020
Chapman & Hall/CRC CRC Press LLC Chapman & Hall |
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| ISBN: | 9780367443207, 9780367439149, 0367443201, 036743914X |
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| Abstract | 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. Several of the scripts are 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. |
|---|---|
| AbstractList | 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.FeaturesGets 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 interfaceRequires no prior knowledge of R, machine learning, or computer programmingOffers over 50 scripts written in R, including several problem-solving templates that, with slight modification, can be used again and againCovers 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 AuthorRichard 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. 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. Several of the scripts are 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. 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. Acknowledgment. Author. Introduction to Machine Learning. Introduction to R. Data Structures and Manipulation. Preparing the Data. Supervised Statistical Techniques. Tree-Based Methods. Rule-Based Techniques. Neural Networks. Formal Evaluation Techniques. Support Vector Machines. Unsupervised Clustering Techniques. A Case Study in Predicting Treatment Outcome. Bibliography. Appendix A: Supplementary Materials and More Datasets. Appendix B: Statistics for Performance Evaluation. Subject Index. Index of R Functions. Script Index. 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. Roiger's 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. The main purpose of the text is to present the student with just enough of the R language, machine learning algorithms, and statistical methodology to set them on their way to a career in data science and machine learning.It is for a beginning course in machine learning, data mining & analytics, data science, or general data analysis. 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. |
| Author | Roiger, Richard J. |
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| Copyright | 2020 Taylor & Francis Group, LLC |
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| Keywords | Agglomerative Clustering Life Insurance Promotion Test Set Accuracy Supervised Models Association Rules Supervised Learner Models Roc Curve Multiple Linear Regression Test Set Instance Arules Package Regression Model Classifier Error Rate Residual Standard Error Magazine Promotion XOR Function Lumbar Extension Missing Data Items Unsupervised Clustering Credit Card Insurance Input Attributes Market Basket Analysis Time Series Gamma Ray Burst Roc Graph Credit Card Promotion Database |
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| Notes | Includes bibliographical references and index |
| OCLC | 1155637833 |
| PQID | EBC6208591 |
| PageCount | 364 346 18 |
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| Publisher | CRC Press Chapman & Hall/CRC CRC Press LLC Chapman & Hall |
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| Snippet | Just Enough R! An Interactive Approach to Machine Learning and Analytics presents just enough of the R language, machine learning algorithms, statistical... The main purpose of the text is to present the student with just enough of the R language, machine learning algorithms, and statistical methodology to set them... |
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| SubjectTerms | Cognitive Artificial Intelligence COMPUTERSCIENCEnetBASE Data Preparation & Mining Data structures (Computer science) INFORMATIONSCIENCEnetBASE Machine Learning Machine Learning - Design Mathematical statistics Mathematical statistics -- Data processing Neural Networks Programming & Programming Languages R (Computer program language) SCI-TECHnetBASE Statistical Computing STATSnetBASE STMnetBASE |
| Subtitle | An Interactive Approach to Machine Learning and Analytics |
| TableOfContents | Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgment -- Author -- Chapter 1 Introduction to Machine Learning -- 1.1 Machine Learning, Statistical Analysis, and Data Science -- 1.2 Machine Learning: A First Example -- 1.2.1 Attribute-Value format -- 1.2.2 A Decision Tree for Diagnosing Illness -- 1.3 Machine Learning Strategies -- 1.3.1 Classicisation -- 1.3.2 Estimation -- 1.3.3 Prediction -- 1.3.4 Unsupervised Clustering -- 1.3.5 Market Basket Analysis -- 1.4 Evaluating Performance -- 1.4.1 Evaluating Supervised Models -- 1.4.2 Two-Class Error Analysis -- 1.4.3 Evaluating Numeric Output -- 1.4.4 Comparing Models by Measuring Lift -- 1.4.5 Unsupervised Model Evaluation -- 1.5 Ethical Issues -- 1.6 Chapter Summary -- 1.7 Key Terms -- Exercises -- Chapter 2 Introduction to R -- 2.1 Introducing R And RStudio -- 2.1.1 Features of R -- 2.1.2 Installing R -- 2.1.3 Installing RStudio -- 2.2 Navigating RStudio -- 2.2.1 The Console -- 2.2.2 The Source Panel -- 2.2.3 The Global Environment -- 2.2.4 Packages -- 2.3 Where's The Data? -- 2.4 Obtaining Help and Additional Information -- 2.5 Summary -- Exercises -- Chapter 3 Data Structures and Manipulation -- 3.1 Data Type -- 3.1.1 Character Data and Factors -- 3.2 Single-Mode Data Structures -- 3.2.1 Vectors -- 3.2.2 Matrices and Arrays -- 3.3 Multimode Data Structures -- 3.3.1 Lists -- 3.3.2 Data Frames -- 3.4 Writing Your Own Functions -- 3.4.1 Writing a Simple Function -- 3.4.2 Conditional Statements -- 3.4.3 Iteration -- 3.4.4 Recursive Programming -- 3.5 Summary -- 3.6 Key Terms -- Exercises -- Chapter 4 Preparing the Data -- 4.1 A Process Model for Knowledge Discovery -- 4.2 Creating A Target Dataset -- 4.2.1 Interfacing R with the Relational Model -- 4.2.2 Additional Sources for Target Data -- 4.3 Data Preprocessing -- 4.3.1 Noisy Data 11.5.6 Agglomerative Clustering Of Credit Screening Data -- 11.6 Chapter Summary -- 11.7 Key Terms -- Exercises -- Chapter 12 A Case Study in Predicting Treatment Outcome -- 12.1 Goal Identification -- 12.2 A Measure of Treatment Success -- 12.3 Target Data Creation -- 12.4 Data Preprocessing -- 12.5 Data Transformation -- 12.6 Data Mining -- 12.6.1 Two-Class Experiments -- 12.7 Interpretation and Evaluation -- 12.7.1 Should Patients Torso Rotate? -- 12.8 Taking Action -- 12.9 Chapter Summary -- Bibliography -- Appendix A: Supplementary Materials and More Datasets -- Appendix B: Statistics for Performance Evaluation -- Subject Index -- Index of R Functions -- Script Index 4.3.2 Preprocessing With R -- 4.3.3 Detecting Outliers -- 4.3.4 Missing Data -- 4.4 Data Transformation -- 4.4.1 Data Normalization -- 4.4.2 Data Type Conversion -- 4.4.3 Attribute and Instance Selection -- 4.4.4 Creating Training and Test Set Data -- 4.4.5 Cross Validation and Bootstrapping -- 4.4.6 Large-Sized Data -- 4.5 Chapter Summary -- 4.6 Key Terms -- Exercises -- Chapter 5 Supervised Statistical Techniques -- 5.1 Simple Linear Regression -- 5.2 Multiple Linear Regression -- 5.2.1 Multiple Linear Regression: An Example -- 5.2.2 Evaluating Numeric Output -- 5.2.3 Training/Test Set Evaluation -- 5.2.4 Using Cross Validation -- 5.2.5 Linear Regression with Categorical Data -- 5.3 Logistic Regression -- 5.3.1 Transforming the Linear Regression Model -- 5.3.2 The Logistic Regression Model -- 5.3.3 Logistic Regression with R -- 5.3.4 Creating a Confusion Matrix -- 5.3.5 Receiver Operating Characteristics (ROC) Curves -- 5.3.6 The Area under an ROC Curve -- 5.4 Naïve Bayes Classifier -- 5.4.1 Bayes Classifier: An Example -- 5.4.2 Zero-Valued Attribute Counts -- 5.4.3 Missing Data -- 5.4.4 Numeric Data -- 5.4.5 Experimenting With Naïve Bayes -- 5.5 Chapter Summary -- 5.6 Key Terms -- Exercises -- Chapter 6 Tree-Based Methods -- 6.1 A Decision Tree Algorithm -- 6.1.1 An Algorithm for Building Decision Trees -- 6.1.2 C4.5 Attribute Selection -- 6.1.3 Other Methods for Building Decision Trees -- 6.2 Building Decision Trees: C5.0 -- 6.2.1 A Decision Tree for Credit Card Promotions -- 6.2.2 Data for Simulating Customer Churn -- 6.2.3 Predicting Customer Churn with C5.0 -- 6.3 Building Decision Trees: Rpart -- 6.3.1 An Rpart Decision Tree for Credit Card Promotions -- 6.3.2 Train and Test Rpart: Churn Data -- 6.3.3 Cross Validation Rpart: Churn Data -- 6.4 Building Decision Trees: J48 -- 6.5 Ensemble Techniques for Improving Performance -- 6.5.1 Bagging 6.5.2 Boosting -- 6.5.3 Boosting: An Example with C5.0 -- 6.5.4 Random Forests -- 6.6 Regression Trees -- 6.7 Chapter Summary -- 6.8 Key Terms -- Exercises -- Chapter 7 Rule-Based Techniques -- 7.1 From Trees to Rules -- 7.1.1 The Spam Email Dataset -- 7.1.2 Spam Email Classification: C5.0 -- 7.2 A Basic Covering Rule Algorithm -- 7.2.1 Generating Covering Rules With JRip -- 7.3 Generating Association Rules -- 7.3.1 Confidence and Support -- 7.3.2 Mining Association Rules: An Example -- 7.3.3 General Considerations -- 7.3.4 Rweka's Apriori Function -- 7.4 Shake, Rattle, and Roll -- 7.5 Chapter Summary -- 7.6 Key Terms -- Exercises -- Chapter 8 Neural Networks -- 8.1 Feed-Forward Neural Networks -- 8.1.1 Neural Network Input Format -- 8.1.2 Neural Network Output Format -- 8.1.3 The Sigmoid Evaluation Function -- 8.2 Neural Network Training: A Conceptual View -- 8.2.1 Supervised Learning with Feed-Forward Networks -- 8.2.2 Unsupervised Clustering With Self-Organizing Maps -- 8.3 Neural Network Explanation -- 8.4 General Considerations -- 8.4.1 Strengths -- 8.4.2 Weaknesses -- 8.5 Neural Network Training: A Detailed View -- 8.5.1 The Backpropagation Algorithm: An Example -- 8.5.2 Kohonen Self-Organizing Maps: An Example -- 8.6 Building Neural Networks with R -- 8.6.1 The Exclusive-OR Function -- 8.6.2 Modeling Exclusive-OR With MLP: Numeric Output -- 8.6.3 Modeling Exclusive-OR With MLP: Categorical Output -- 8.6.4 Modeling Exclusive-OR With Neuralnet: Numeric Output -- 8.6.5 Modeling Exclusive-OR With Neuralnet: Categorical Output -- 8.6.6 Classifying Satellite Image Data -- 8.6.7 Testing For Diabetes -- 8.7 Neural Net Clustering For Attribute Evaluation -- 8.8 Times Series Analysis -- 8.8.1 Stock Market Analytics -- 8.8.2 Time Series Analysis: An Example -- 8.8.3 The Target Data -- 8.8.4 Modeling the Time Series -- 8.8.5 General Considerations 8.9 Chapter Summary -- 8.10 Key Terms -- Exercises -- Chapter 9 Formal Evaluation Techniques -- 9.1 What Should Be Evaluated? -- 9.2 Tools for Evaluation -- 9.2.1 Single-Valued Summary Statistics -- 9.2.2 The Normal Distribution -- 9.2.3 Normal Distributions and Sample Means -- 9.2.4 A Classical Model for Hypothesis Testing -- 9.3 Computing Test Set Confidence Intervals -- 9.4 Comparing Supervised Models -- 9.4.1 Comparing the Performance of Two Models -- 9.4.2 Comparing the Performance of Two or More Models -- 9.5 Confidence Intervals for Numeric Output -- 9.6 Chapter Summary -- 9.7 Key Terms -- Exercises -- Chapter 10 Support Vector Machines -- 10.1 Linearly Separable Classes -- 10.2 The Nonlinear Case -- 10.3 Experimenting With Linearly Separable Data -- 10.4 Microarray Data Mining -- 10.4.1 DNA and Gene Expression -- 10.4.2 Preprocessing Microarray Data: Attribute Selection -- 10.4.3 Microarray Data Mining: Issues -- 10.5 A Microarray Application -- 10.5.1 Establishing a Benchmark -- 10.5.2 Attribute Elimination -- 10.6 Chapter Summary -- 10.7 Key Terms -- Exercises -- Chapter 11 Unsupervised Clustering Techniques -- 11.1 The K-Means Algorithm -- 11.1.1 An Example Using K-Means -- 11.1.2 General Considerations -- 11.2 Agglomerative Clustering -- 11.2.1 Agglomerative Clustering: An Example -- 11.2.2 General Considerations -- 11.3 Conceptual Clustering -- 11.3.1 Measuring Category Utility -- 11.3.2 Conceptual Clustering: An Example -- 11.3.3 General Considerations -- 11.4 Expectation Maximization -- 11.5 Unsupervised Clustering With R -- 11.5.1 Supervised Learning for Cluster Evaluation -- 11.5.2 Unsupervised Clustering For Attribute Evaluation -- 11.5.3 Agglomerative Cluster: A Simple Example -- 11.5.4 Agglomerative Clustering of Gamma-Ray Burst data -- 11.5.5 Agglomerative Clustering Of Cardiology Patient Data |
| Title | Just Enough R |
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