000			04669nam a22005175i 4500
001			978-3-031-01899-2
003			DE-He213
005			20240730163443.0
007			cr nn 008mamaa
008			220601s2010 sz | s |||| 0|eng d
020			_a9783031018992 _9978-3-031-01899-2
024	7		_a10.1007/978-3-031-01899-2 _2doi
050		4	_aQA76.9.D343
072		7	_aUNF _2bicssc
072		7	_aUYQE _2bicssc
072		7	_aCOM021030 _2bisacsh
072		7	_aUNF _2thema
072		7	_aUYQE _2thema
082	0	4	_a006.312 _223
100	1		_aSeni, Giovanni. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978630
245	1	0	_aEnsemble Methods in Data Mining _h[electronic resource] : _bImproving Accuracy Through Combining Predictions / _cby Giovanni Seni, John Elder.
250			_a1st ed. 2010.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2010.
300			_aXVI, 138 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSynthesis Lectures on Data Mining and Knowledge Discovery, _x2151-0075
505	0		_aEnsembles Discovered -- Predictive Learning and Decision Trees -- Model Complexity, Model Selection and Regularization -- Importance Sampling and the Classic Ensemble Methods -- Rule Ensembles and Interpretation Statistics -- Ensemble Complexity.
520			_aEnsemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods -- bagging, random forests, and boosting -- to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity. This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. The authorsare industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners. Table of Contents: Ensembles Discovered / Predictive Learning and Decision Trees / Model Complexity, Model Selection and Regularization / Importance Sampling and the Classic Ensemble Methods / Rule Ensembles and Interpretation Statistics / Ensemble Complexity.
650		0	_aData mining. _93907
650		0	_aStatistics . _931616
650	1	4	_aData Mining and Knowledge Discovery. _978631
650	2	4	_aStatistics. _914134
700	1		_aElder, John. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _978632
710	2		_aSpringerLink (Online service) _978633
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031007712
776	0	8	_iPrinted edition: _z9783031030277
830		0	_aSynthesis Lectures on Data Mining and Knowledge Discovery, _x2151-0075 _978634
856	4	0	_uhttps://doi.org/10.1007/978-3-031-01899-2
912			_aZDB-2-SXSC
942			_cEBK
999			_c84623 _d84623