000			03889cam a2200589Ki 4500
001			9780429297595
003			FlBoTFG
005			20220724194335.0
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 _qelectronic book _qMobipocket
020			_a1000176827 _qelectronic book _qMobipocket
020			_a9781000176834 _qelectronic book _qEPUB
020			_a1000176835 _qelectronic book _qEPUB
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