000			01805cam a2200301Ii 4500
001			on1099869989
003			OCoLC
007			ta
008			210114s2020 flua b 001 0 eng d
020			_a1138079227
020			_a9781138079229
035			_a(OCoLC)1099869989
050			_aTJ217.6 _b.K84 2020
100	1		_aKuhn, Max.
245	1	0	_aFeature engineering and selection : _ba practical approach for predictive models / _cMax Kuhn, Kjell Johnson.
260			_aBoca Raton : _bCRC Press, Taylor & Francis Group, _cc2020.
300			_axv, 297 p. : _bill.
490	1		_aChapman & Hall/CRC data science series
504			_aIncludes bibliographical references and index.
505	0		_aIllustrative example: predicting risk of ischemic stroke -- A review of the predictive modeling process -- Exploratory visualizations -- Encoding categorical predictors -- Engineering numeric predictors -- Detecting interaction effects -- Handling missing data -- Working with profile data -- Feature selection overview -- Greedy search methods -- Global search methods.
520			_aThe process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for finding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
650		4	_aPredictive control _xData processing.
650		4	_aPredictive control _xMathematical models.
650		4	_aR (Computer program language)
700	1		_aJohnson, Kjell.
830		0	_aChapman & Hall/CRC data science series.
942			_2lcc _cBK
999			_c260 _d260