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Supervised machine learning : optimization framework and applications with SAS and R / Tanya Kolosova and Samuel Berestizhevsky.

By: Kolosova, Tanya [author.]Contributor(s): Berestizhevsky, Samuel [author.]Material type: Text

TextPublisher: Boca Raton, FL : CRC Press, 2021Copyright date: ©2021Edition: First editionDescription: 1 online resource (xxiv, 160 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9780429297595; 0429297599; 9781000176810; 1000176819; 9781000176827; 1000176827; 9781000176834; 1000176835Subject(s): Supervised learning (Machine learning) | Program transformation (Computer programming) | SAS (Computer program language) | R (Computer program language) | COMPUTERS / Machine Theory | COMPUTERS / Mathematical & Statistical SoftwareDDC classification: 006.3/1 LOC classification: Q325.75 | .K65 2021ebOnline resources: Taylor & Francis | OCLC metadata license agreement

Contents:

IntroductionPART 1Introduction to the AI frameworkSupervised Machine Learning and Its Deployment in SAS and RBootstrap methods and Its Deployment in SAS and ROutliers Detection and Its Deployment in SAS and RDesign of Experiment and Its Deployment in SAS and RPART IIIntroduction to the SAS and R based table-driven environmentInput Data componentDesign of Experiment for Machine-Learning component"Contaminated" Training Datasets ComponentPART IIIInsurance Industry: Underwriters decision-making processInsurance Industry: Claims Modeling and PredictionIndex

Summary: 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

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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

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