Supervised Machine Learning: Optimization Framework and Applications with SAS and R

Kolosova, Tanya, Berestizhevsky, Samuel

Supervised Machine Learning: Optimization Framework and Applications with SAS and R

相關主題

商品描述

AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers.

Key Features:

 

 

 

 

 

  • Using ML methods by itself doesn't ensure building classifiers that generalize well for new data
  • Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments
  • Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias
  • Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks
  • Computer programs in R and SAS that create AI framework are available on GitHub

商品描述(中文翻譯)

AI框架旨在解決監督式學習方法在實際應用中的偏差-方差折衷問題。該AI框架包括使用自助法創建多個具有不同特徵的訓練和測試數據集，設計和分析統計實驗以識別最佳特徵子集和最佳超參數，以及使用數據污染來測試分類器的韌性。

主要特點：
- 單獨使用機器學習方法並不能確保構建出對新數據具有良好泛化能力的分類器。
- 使用設計和分析統計實驗的方法可以解決識別最佳特徵子集和機器學習方法最佳超參數的問題。
- 使用自助法大量抽樣訓練和測試數據集，包括具有不同數據特徵的數據集（例如：受污染的訓練集），可以處理偏差問題。
- 基於SAS的表格驅動環境的開發，可以管理與提出的AI框架相關的所有元數據，並與R庫實現互操作性，以完成各種統計和機器學習任務。
- 在GitHub上提供了創建AI框架的R和SAS計算機程序。

作者簡介

Tanya Kolosova is a statistician, software engineer, an educator, and a co-author of two books on statistical analysis and metadata-based applications development using SAS. Tanya is an actionable analytics expert, she has extensive knowledge of software development methods and technologies, artificial intelligence methods and algorithms, and statistically designed experiments.

Samuel Berestizhevsky is a statistician, researcher and software engineer. Together with Tanya, Samuel co-authored two books on statistical analysis and metadata-based applications development using SAS. Samuel is an innovator and an expert in the area of automated actionable analytics and artificial intelligence solutions. His extensive knowledge of software development methods, technologies and algorithms allows him to develop solutions on the cutting edge of science.

作者簡介(中文翻譯)

Tanya Kolosova是一位統計學家、軟體工程師、教育家，並且是兩本關於使用SAS進行統計分析和基於元數據的應用開發的合著者。Tanya是一位可行的分析專家，她對軟體開發方法和技術、人工智慧方法和算法以及統計設計實驗有豐富的知識。

Samuel Berestizhevsky是一位統計學家、研究員和軟體工程師。與Tanya一起，Samuel合著了兩本關於使用SAS進行統計分析和基於元數據的應用開發的書籍。Samuel是一位創新者，也是自動可行分析和人工智慧解決方案領域的專家。他對軟體開發方法、技術和算法的廣泛知識使他能夠開發處於科學前沿的解決方案。

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