Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis

Simske, Steven


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


《Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis》提供了一套詳盡的模式,供數據科學家在任何基於機器學習的數據分析任務中使用。這本書幾乎可以確保至少有一個模式會比傳統分析方法更好地改善整體系統行為。這本書是對分析的「元」,詳細介紹了一般分析,讓讀者能夠參與並理解混合或元方法。這本書與機器翻譯、機器人技術、生物和社會科學、醫療保健資訊學、經濟學、商業和金融等領域有關。此外,其中的分析方法可應用於從警察部門到體育分析師的預測算法。