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
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Acknowledgments
-
Chapter 1: Introduction, overview, and applications
- Abstract
- 1.1 Introduction
- 1.2 Why is this book important?
- 1.3 Organization of the book
- 1.4 Informatics
- 1.5 Statistics for analytics
- 1.6 Algorithms for analytics
- 1.7 Machine learning
- 1.8 Artificial intelligence
- 1.9 A platform for building a classifier from the ground up (binary case)
- 1.10 A platform for building a classifier from the ground up (general case)
- 1.11 Summary
- Chapter 2: Ground truthing
- Chapter 3: Experimental design
-
Chapter 4: Meta-analytic design patterns
- Abstract
- 4.1 Introduction
- 4.2 Cumulative response patterns
- 4.3 Optimization of analytics
- 4.4 Model agreement patterns
- 4.5 Co-occurrence and similarity patterns
- 4.6 Sensitivity analysis patterns
- 4.7 Confusion matrix patterns
- 4.8 Entropy patterns
- 4.9 Independence pattern
- 4.10 Functional NLP patterns (macro-feedback)
- 4.11 Summary
- Chapter 5: Sensitivity analysis and big system engineering
- Chapter 6: Multipatch predictive selection
-
Chapter 7: Modeling and model fitting
- Abstract
- 7.1 Introduction
- 7.2 Chemistry analogues for analytics
- 7.3 Organic chemistry analogues for analytics
- 7.4 Immunological and biological analogues for analytics
- 7.5 Anonymization analogues for model design and fitting
- 7.6 LSE, error variance, and entropy: Goodness of fit
- 7.7 Make mine multiple models!
- 7.8 Summary
- Chapter 8: Synonym-antonym and reinforce-void patterns
- Chapter 9: Analytics around analytics
- Chapter 10: System design optimization
- Chapter 11: Aleatory and expert system techniques
- Chapter 12: Application I: Topics and challenges in machine translation, robotics, and biological sciences
- Chapter 13: Application II: Medical and health-care informatics, economics, business, and finance
- Chapter 14: Discussion, conclusions, and the future of data
- Index
Product information
- Title: Meta-Analytics
- Author(s):
- Release date: March 2019
- Publisher(s): Morgan Kaufmann
- ISBN: 9780128146248
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