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001 9780429297595
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006 m o d
007 cr cnu---unuuu
008 201023t20212021flu ob 000 0 eng d
040 _aOCoLC-P
_beng
_erda
_epn
_cOCoLC-P
020 _a9780429297595
_qelectronic book
020 _a0429297599
_qelectronic book
020 _a9781000176810
_qelectronic book
020 _a1000176819
_qelectronic book
020 _a9781000176827
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020 _a1000176827
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020 _a9781000176834
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020 _a1000176835
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020 _z9780367538828
020 _z9780367277321
035 _a(OCoLC)1201337174
035 _a(OCoLC-P)1201337174
050 4 _aQ325.75
_b.K65 2021eb
072 7 _aCOM
_x037000
_2bisacsh
072 7 _aCOM
_x077000
_2bisacsh
072 7 _aPBT
_2bicssc
082 0 4 _a006.3/1
_223
100 1 _aKolosova, Tanya,
_eauthor.
245 1 0 _aSupervised machine learning :
_boptimization framework and applications with SAS and R /
_cTanya Kolosova and Samuel Berestizhevsky.
250 _aFirst edition.
264 1 _aBoca Raton, FL :
_bCRC Press,
_c2021.
264 4 _c©2021
300 _a1 online resource (xxiv, 160 pages).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
505 0 _aIntroductionPART 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
520 _aAI 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
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aSupervised learning (Machine learning)
650 0 _aProgram transformation (Computer programming)
650 0 _aSAS (Computer program language)
650 0 _aR (Computer program language)
650 7 _aCOMPUTERS / Machine Theory
_2bisacsh
650 7 _aCOMPUTERS / Mathematical & Statistical Software
_2bisacsh
700 1 _aBerestizhevsky, Samuel,
_eauthor.
856 4 0 _3Read Online
_uhttps://www.taylorfrancis.com/books/9780429297595
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942 _2lcc
_cEBK
999 _c16493
_d16493