Название: Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis Автор: Steven Simske
Издательство: Morgan Kaufmann
Год: 2019
Страниц: 327
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB, 10.9 MB
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.
A note on what is meant by meta-analytics is worth providing. Essentially, “meta-analysis” has two broad fields of study/application:
• 1. Meta- in the sense of meta-algorithmics, where we are combining two or more analytic techniques (algorithms, processes, services, systems, etc.) to obtain improved analytic output.
• 2. Meta- in the sense of being outside, additional, and augmentative to pure analytics, which includes fields such as testing, ground truthing, training, and sensitivity analysis and optimization of system design.
With this perspective, analytics is more than just simply machine learning: it is also learning in the correct order. It is not only knowledge extraction but also extraction of knowledge in the correct order. It is not only creating information but also creating information in the correct order. This means that analytics is more than simple descriptive or quantitative information. It is meant to extract and tell a story about the data that someone skilled in the field would be able to provide, including modifying the analysis in light of changing data and context for the data.
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
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