02095cam a22003498i 45000010009000000050017000090080041000260200018000670200018000850400013001030820018001161000027001342450089001612600035002503000016002854900024003015000051003255200803003765460008011796500059011876500057012466500050013036500032013536500031013856500034014166500034014507000024014849420007015089990019015159520109015349520102016432208350420260716150450.0210605s2022    flu      b    001 0 eng    a9780367554187  a9780367554194  cPK-LaUMT00a006.35bHVI-S1 aHvitfeldt, Emil91372510aSupervised machine learning for text analysis in R /cEmil Hvitfeldt and Julia Silge  aBoca Raton :bCRC Press,c2022  axix, 381 p.0 aData science series  aIncludes bibliographical references and index.  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  aEng 0aComputational linguisticsxStatistical methods.913726 0aNatural language processing (Computer science)95560 0aSupervised learning (Machine learning)913727 0aPredictive analytics.95545 0aRegression analysis.95062 0aDiscriminant analysis.913728 0aR (Computer program language)1 aSilge, Julia913729  cBK  c125682d125682  00104070a01b01d2022-02-21l2o006.35 HVI-Sp141973r2025-03-03 14:57:10s2025-02-12w2022-02-21yBK  00104070a01b01d2026-07-16l0o006.35 HVI-Sp153369r2026-07-16 15:05:12tC.2w2026-07-16yBK