000			04405cam a2200601Ki 4500
001			9781003006695
003			FlBoTFG
005			20240213122830.0
006			m o d
007			cr cnu|||unuuu
008			200528s2020 xx o 000 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9781003006695 _q(electronic bk.)
020			_a1003006698 _q(electronic bk.)
020			_a9781000073560 _q(electronic bk. : EPUB)
020			_a1000073564 _q(electronic bk. : EPUB)
020			_z9780367443207
020			_z9780367439149
020			_a9781000073539 _q(electronic bk. : Mobipocket)
020			_a100007353X _q(electronic bk. : Mobipocket)
020			_a9781000073508 _q(electronic bk. : PDF)
020			_a1000073505 _q(electronic bk. : PDF)
020			_z036743914X
020			_z0367443201
035			_a(OCoLC)1155637833 _z(OCoLC)1156472803
035			_a(OCoLC-P)1155637833
050		4	_aQA276.45.R3 _bR65 2020
072		7	_aCOM _x018000 _2bisacsh
072		7	_aCOM _x021030 _2bisacsh
072		7	_aCOM _x021000 _2bisacsh
072		7	_aUYQ _2bicssc
082	0	4	_a005.13/3 _223
100	1		_aRoiger, Richard J.
245	1	0	_aJust Enough R! : _bAn Interactive Approach to Machine Learning and Analytics.
250			_aFirst edition.
264		1	_a[Place of publication not identified] : _bChapman and Hall/CRC, _c2020.
300			_a1 online resource (xviii, 346 pages).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
505	0		_aPreface. Acknowledgment. Author. Introduction to Machine Learning. Introduction to R. Data Structures and Manipulation. Preparing the Data. Supervised Statistical Techniques. Tree-Based Methods. Rule-Based Techniques. Neural Networks. Formal Evaluation Techniques. Support Vector Machines. Unsupervised Clustering Techniques. A Case Study in Predicting Treatment Outcome. Bibliography. Appendix A: Supplementary Materials and More Datasets. Appendix B: Statistics for Performance Evaluation. Subject Index. Index of R Functions. Script Index.
520			_aJust Enough R! An Interactive Approach to Machine Learning and Analytics presents just enough of the R language, machine learning algorithms, statistical methodology, and analytics for the reader to learn how to find interesting structure in data. The approach might be called "seeing then doing" as it first gives step-by-step explanations using simple, understandable examples of how the various machine learning algorithms work independent of any programming language. This is followed by detailed scripts written in R that apply the algorithms to solve nontrivial problems with real data. The script code is provided, allowing the reader to execute the scripts as they study the explanations given in the text. Features Gets you quickly using R as a problem-solving tool Uses RStudio's integrated development environment Shows how to interface R with SQLite Includes examples using R's Rattle graphical user interface Requires no prior knowledge of R, machine learning, or computer programming Offers over 50 scripts written in R, including several problem-solving templates that, with slight modification, can be used again and again Covers the most popular machine learning techniques, including ensemble-based methods and logistic regression Includes end-of-chapter exercises, many of which can be solved by modifying existing scripts Includes datasets from several areas, including business, health and medicine, and science About the Author Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato, where he taught and performed research in the Computer and Information Science Department for over 30 years.
588			_aOCLC-licensed vendor bibliographic record.
650		7	_aCOMPUTERS / Data Processing / General _2bisacsh
650		7	_aCOMPUTERS / Database Management / Data Mining _2bisacsh
650		7	_aCOMPUTERS / Database Management / General _2bisacsh
650		0	_aR (Computer program language)
650		0	_aMachine learning.
650		0	_aData structures (Computer science)
650		0	_aMathematical statistics _xData processing.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003006695
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c5648 _d5648