03869cam a2200577Ki 4500 9780429297595 FlBoTFG 20260210180759.0 m o d cr cnu---unuuu 201023t20212021flu ob 000 0 eng d OCoLC-P eng rda pn OCoLC-P 9780429297595 electronic book 0429297599 electronic book 9781000176810 electronic book 1000176819 electronic book 9781000176827 electronic book Mobipocket 1000176827 electronic book Mobipocket 9781000176834 electronic book EPUB 1000176835 electronic book EPUB 9780367538828 9780367277321 (OCoLC)1201337174 (OCoLC-P)1201337174 Q325.75 .K65 2021eb COM 037000 bisacsh COM 077000 bisacsh PBT bicssc 006.3/1 23 Kolosova, Tanya, author. Supervised machine learning : optimization framework and applications with SAS and R / Tanya Kolosova and Samuel Berestizhevsky. First edition. Boca Raton, FL : CRC Press, 2021. ©2021 1 online resource (xxiv, 160 pages). text txt rdacontent computer c rdamedia online resource cr rdacarrier 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 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 OCLC-licensed vendor bibliographic record. Supervised learning (Machine learning) Program transformation (Computer programming) SAS (Computer program language) R (Computer program language) COMPUTERS / Machine Theory bisacsh COMPUTERS / Mathematical & Statistical Software bisacsh Berestizhevsky, Samuel, author. Taylor & Francis https://www.taylorfrancis.com/books/9780429297595 OCLC metadata license agreement http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf 90937 90936