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Supervised Machine Learning at Meripustak

Supervised Machine Learning

Books from same Author: Kolosova Tanya

Books from same Publisher: T&F/Crc Press

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    • General Information  
    Author(s)Kolosova Tanya
    PublisherT&F/Crc Press
    Edition1st Edition
    ISBN9780367538828
    Pages182
    BindingSoftcover
    LanguageEnglish
    Publish YearApril 2022

    Description

    T&F/Crc Press Supervised Machine Learning by Kolosova Tanya

    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 dataIdentifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experimentsUsing a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with biasDeveloping 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 tasksComputer programs in R and SAS that create AI framework are available on GitHub