O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Meta-Analytics

Book Description

Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional analytics approaches. The book is ‘meta’ to analytics, covering general analytics in sufficient detail for readers to engage with, and understand, hybrid or meta- approaches. The book has relevance to machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance.

Inn addition, the analytics within can be applied to predictive algorithms for everyone from police departments to sports analysts.

  • Provides comprehensive and systematic coverage of machine learning-based data analysis tasks
  • Enables rapid progress towards competency in data analysis techniques
  • Gives exhaustive and widely applicable patterns for use by data scientists
  • Covers hybrid or ‘meta’ approaches, along with general analytics
  • Lays out information and practical guidance on data analysis for practitioners working across all sectors

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. Acknowledgments
  7. Chapter 1: Introduction, overview, and applications
    1. Abstract
    2. 1.1 Introduction
    3. 1.2 Why is this book important?
    4. 1.3 Organization of the book
    5. 1.4 Informatics
    6. 1.5 Statistics for analytics
    7. 1.6 Algorithms for analytics
    8. 1.7 Machine learning
    9. 1.8 Artificial intelligence
    10. 1.9 A platform for building a classifier from the ground up (binary case)
    11. 1.10 A platform for building a classifier from the ground up (general case)
    12. 1.11 Summary
  8. Chapter 2: Ground truthing
    1. Abstract
    2. 2.1 Introduction
    3. 2.2 Pre-validation
    4. 2.3 Optimizing settings from training data
    5. 2.4 Learning how to Learn
    6. 2.5 Deep learning to deep unlearning
    7. 2.6 Summary
  9. Chapter 3: Experimental design
    1. Abstract
    2. 3.1 Introduction
    3. 3.2 Data normalization
    4. 3.3 Designs for the pruning of aging data
    5. 3.4 Systems of systems
    6. 3.5 Summary
  10. Chapter 4: Meta-analytic design patterns
    1. Abstract
    2. 4.1 Introduction
    3. 4.2 Cumulative response patterns
    4. 4.3 Optimization of analytics
    5. 4.4 Model agreement patterns
    6. 4.5 Co-occurrence and similarity patterns
    7. 4.6 Sensitivity analysis patterns
    8. 4.7 Confusion matrix patterns
    9. 4.8 Entropy patterns
    10. 4.9 Independence pattern
    11. 4.10 Functional NLP patterns (macro-feedback)
    12. 4.11 Summary
  11. Chapter 5: Sensitivity analysis and big system engineering
    1. Abstract
    2. 5.1 Introduction
    3. 5.2 Sensitivity analysis of the data set itself
    4. 5.3 Sensitivity analysis of the solution model
    5. 5.4 Sensitivity analysis of the individual algorithms
    6. 5.5 Sensitivity analysis of the hybrid algorithmics
    7. 5.6 Sensitivity analysis of the path to the current state
    8. 5.7 Summary
  12. Chapter 6: Multipatch predictive selection
    1. Abstract
    2. 6.1 Introduction
    3. 6.2 Predictive selection
    4. 6.3 Means of predicting
    5. 6.4 Means of selecting
    6. 6.5 Multi-path approach
    7. 6.6 Applications
    8. 6.7 Sensitivity analysis
    9. 6.8 Summary
  13. Chapter 7: Modeling and model fitting
    1. Abstract
    2. 7.1 Introduction
    3. 7.2 Chemistry analogues for analytics
    4. 7.3 Organic chemistry analogues for analytics
    5. 7.4 Immunological and biological analogues for analytics
    6. 7.5 Anonymization analogues for model design and fitting
    7. 7.6 LSE, error variance, and entropy: Goodness of fit
    8. 7.7 Make mine multiple models!
    9. 7.8 Summary
  14. Chapter 8: Synonym-antonym and reinforce-void patterns
    1. Abstract
    2. 8.1 Introduction
    3. 8.2 Synonym-antonym patterns
    4. 8.3 Reinforce-void patterns
    5. 8.4 Broader applicability of these patterns
    6. 8.5 Summary
  15. Chapter 9: Analytics around analytics
    1. Abstract
    2. 9.1 Introduction
    3. 9.2 Analytics around analytics
    4. 9.3 Optimizing settings from training data
    5. 9.4 Hybrid methods
    6. 9.5 Other areas for investigation around the analytics
    7. 9.6 Summary
  16. Chapter 10: System design optimization
    1. Abstract
    2. 10.1 Introduction
    3. 10.2 Module optimization
    4. 10.3 Clustering and regularization
    5. 10.4 Analytic system optimization
    6. 10.5 Summary
  17. Chapter 11: Aleatory and expert system techniques
    1. Abstract
    2. 11.1 Introduction
    3. 11.2 Revisiting two earlier aleatory patterns
    4. 11.3 Adding random elements for testing
    5. 11.4 Hyperspectral aleatory approaches
    6. 11.5 Other aleatory applications in machine and statistical learning
    7. 11.6 Expert system techniques
    8. 11.7 Summary
  18. Chapter 12: Application I: Topics and challenges in machine translation, robotics, and biological sciences
    1. Abstract
    2. 12.1 Introduction
    3. 12.2 Machine translation
    4. 12.3 Robotics
    5. 12.4 Biological sciences
    6. 12.5 Summary
  19. Chapter 13: Application II: Medical and health-care informatics, economics, business, and finance
    1. Abstract
    2. 13.1 Introduction
    3. 13.2 Healthcare
    4. 13.3 Economics
    5. 13.4 Business and finance
    6. 13.5 Summary
    7. 13.6 Postscript: Psychology
  20. Chapter 14: Discussion, conclusions, and the future of data
    1. Abstract
    2. 14.1 Chapter 1
    3. 14.2 Chapter 2
    4. 14.3 Chapter 3
    5. 14.4 Chapter 4
    6. 14.5 Chapter 5
    7. 14.6 Chapter 6
    8. 14.7 Chapter 7
    9. 14.8 Chapter 8
    10. 14.9 Chapter 9
    11. 14.10 Chapter 10
    12. 14.11 Chapter 11
    13. 14.12 Chapter 12
    14. 14.13 Chapter 13
    15. 14.14 The future of meta-analytics
  21. Index