000			01860cam a22003258i 4500
001			22083504
005			20260716150450.0
008			210605s2022 flu b 001 0 eng
020			_a9780367554187
020			_a9780367554194
040			_cPK-LaUMT
082	0	0	_a006.35 _bHVI-S
100	1		_aHvitfeldt, Emil _913725
245	1	0	_aSupervised machine learning for text analysis in R / _cEmil Hvitfeldt and Julia Silge
260			_aBoca Raton : _bCRC Press, _c2022
300			_axix, 381 p.
490	0		_aData science series
500			_aIncludes bibliographical references and index.
520			_a"Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing"-- Provided by publisher
546			_aEng
650		0	_aComputational linguistics _xStatistical methods. _913726
650		0	_aNatural language processing (Computer science) _95560
650		0	_aSupervised learning (Machine learning) _913727
650		0	_aPredictive analytics. _95545
650		0	_aRegression analysis. _95062
650		0	_aDiscriminant analysis. _913728
650		0	_aR (Computer program language)
700	1		_aSilge, Julia _913729
942			_cBK
999			_c125682 _d125682