Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis
內容描述
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 tasksEnables rapid progress towards competency in data analysis techniquesGives exhaustive and widely applicable patterns for use by data scientistsCovers hybrid or 'meta' approaches, along with general analyticsLays out information and practical guidance on data analysis for practitioners working across all sectors
