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• Ensembles Discovered Predictive Learning and Decision Trees Model Complexity, Model Selection and Regularization Importance Sampling and the Classic Ensemble Methods Rule Ensembles and Interpretation Statistics Ensemble Complexity.
• (source: Nielsen Book Data)

Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges - from investment timing to drug discovery, and fraud detection to recommendation systems - where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization - today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods - bagging, random forests, and boosting - to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners.
(source: Nielsen Book Data)

• Ensembles Discovered Predictive Learning and Decision Trees Model Complexity, Model Selection and Regularization Importance Sampling and the Classic Ensemble Methods Rule Ensembles and Interpretation Statistics Ensemble Complexity.
• (source: Nielsen Book Data)

Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges - from investment timing to drug discovery, and fraud detection to recommendation systems - where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization - today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods - bagging, random forests, and boosting - to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners.
(source: Nielsen Book Data)

• PART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process
• Chapter 1. History
• The Phases of Data Analysis throughout the Ages
• Chapter 2. Theory
• Chapter 3. The Data Mining Process
• Chapter 4. Data Understanding and Preparation
• Chapter 5. Feature Selection
• Selecting the Best Variables
• Chapter 6: Accessory Tools and Advanced Features in Data
• PART II:
• The Algorithms in Data Mining and Text Mining, and the Organization of the Three most common Data Mining Tools
• Chapter 7. Basic Algorithms
• Chapter 8: Advanced Algorithms
• Chapter 9. Text Mining
• Chapter 10. Organization of 3 Leading Data Mining Tools
• Chapter 11. Classification Trees = Decision Trees
• Chapter 12. Numerical Prediction (Neural Nets and GLM)
• Chapter 13. Model Evaluation and Enhancement
• Chapter 14. Medical Informatics
• Chapter 15. Bioinformatics
• Chapter 16. Customer Response Models
• Chapter 17. Fraud Detection
• PART III: Tutorials
• Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining Analyses
• Listing of Guest Authors of the Tutorials
• Tutorials within the book pages:
• How to use the DMRecipe
• Aviation Safety using DMRecipe
• Movie Box-Office Hit Prediction using SPSS CLEMENTINE
• Bank Financial data
• using SAS-EM
• Credit Scoring
• CRM Retention using CLEMENTINE
• Automobile
• Cars
• Text Mining
• Quality Control using Data Mining
• Three integrated tutorials from different domains, but all using C & RT to predict and display possible structural relationships among data:
• Business Administration in a Medical Industry
• Clinical Psychology- Finding Predictors of Correct Diagnosis
• Education
• Leadership Training: for Business and Education
• Additional tutorials are available either on the accompanying CD-DVD, or the Elsevier Web site for this book
• Listing of Tutorials on Accompanying CD
• PART IV: Paradox of Complex Models; using the "right model for the right use", on-going development, and the Future.
• Chapter 18: Paradox of Ensembles and Complexity
• Chapter 19: The Right Model for the Right Use
• Chapter 20: The Top 10 Data Mining Mistakes
• Chapter 21: Prospect for the Future
• Developing Areas in Data Mining.

The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce.

• Preface Forwards Introduction PART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process Chapter 1. History - The Phases of Data Analysis throughout the Ages Chapter 2. Theory Chapter 3. The Data Mining Process Chapter 4. Data Understanding and Preparation Chapter 5. Feature Selection - Selecting the Best Variables Chapter 6: Accessory Tools and Advanced Features in Data PART II: - The Algorithms in Data Mining and Text Mining, and the Organization of the Three most common Data Mining Tools Chapter 7. Basic Algorithms Chapter 8: Advanced Algorithms Chapter 9. Text Mining Chapter 10. Organization of 3 Leading Data Mining Tools Chapter 11. Classification Trees = Decision Trees Chapter 12. Numerical Prediction (Neural Nets and GLM Chapter 13. Model Evaluation and Enhancement Chapter 14. Medical Informatics Chapter 15. Bioinformatics Chapter 16. Customer Response Models Chapter 17. Fraud Detection PART III: Tutorials - Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining Analyses Tutorials PART IV: Paradox of Complex Models
• using the "right model for the right use", on-going development, and the Future. Chapter 18: Paradox of Ensembles and Complexity Chapter 19: The Right Model for the Right Use Chapter 20: The Top 10 Data Mining Mistakes Chapter 21: Prospect for the Future - Developing Areas in Data Mining Chapter 22: Summary GLOSSARY of STATISICAL and DATA MINING TERMS INDEX CD - With Additional Tutorials, data sets, Power Points, and Data Mining software.
• (source: Nielsen Book Data)

The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions.
(source: Nielsen Book Data)

• PART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process
• Chapter 1. History - The Phases of Data Analysis throughout the Ages
• Chapter 2. Theory
• Chapter 3. The Data Mining Process
• Chapter 4. Data Understanding and Preparation
• Chapter 5. Feature Selection - Selecting the Best Variables
• Chapter 6: Accessory Tools and Advanced Features in Data
• PART II: - The Algorithms in Data Mining and Text Mining, and the Organization of the Three most common Data Mining Tools
• Chapter 7. Basic Algorithms
• Chapter 8: Advanced Algorithms
• Chapter 9. Text Mining
• Chapter 10. Organization of 3 Leading Data Mining Tools
• Chapter 11. Classification Trees = Decision Trees
• Chapter 12. Numerical Prediction (Neural Nets and GLM)
• Chapter 13. Model Evaluation and Enhancement
• Chapter 14. Medical Informatics
• Chapter 15. Bioinformatics
• Chapter 16. Customer Response Models
• Chapter 17. Fraud Detection
• PART III: Tutorials - Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining Analyses
• Listing of Guest Authors of the Tutorials
• Tutorials within the book pages:
• How to use the DMRecipe
• Aviation Safety using DMRecipe
• Movie Box-Office Hit Prediction using SPSS CLEMENTINE
• Bank Financial data - using SAS-EM
• Credit Scoring
• CRM Retention using CLEMENTINE
• Automobile - Cars - Text Mining
• Quality Control using Data Mining
• Three integrated tutorials from different domains, but all using C&RT to predict and display possible structural relationships among data:
• Business Administration in a Medical Industry
• Clinical Psychology- Finding Predictors of Correct Diagnosis
• Education - Leadership Training: for Business and Education
• Additional tutorials are available either on the accompanying CD-DVD, or the Elsevier Web site for this book
• Listing of Tutorials on Accompanying CD
• PART IV: Paradox of Complex Models; using the "right model for the right use", on-going development, and the Future.
• Chapter 18: Paradox of Ensembles and Complexity
• Chapter 19: The Right Model for the Right Use
• Chapter 20: The Top 10 Data Mining Mistakes
• Chapter 21: Prospect for the Future - Developing Areas in Data Mining.

The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions.
(source: Nielsen Book Data)
"The Handbook of Statistical Analysis and Data Mining Applications" is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. Written 'By Practitioners for Practitioners', non-technical explanations build understanding without jargon and equations. Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models using Statistica, SAS and SPSS software. The book offers practical advice from successful real-world implementations. It includes extensive case studies, examples, MS PowerPoint slides and datasets. A CD-DVD with valuable fully-working 90-day software is included. 'Complete Data Miner - QC-Miner - Text Miner' is bound with book.
(source: Nielsen Book Data)