Machine Learning in Action.pdf

Machine Learning in Action.pdf

ID:33863996

大小:6.58 MB

页数:382页

时间:2019-02-28

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1、INACTIONPeterHarringtonMANNINGMachineLearninginActionMachineLearninginActionPETERHARRINGTONMANNINGShelterIslandForonlineinformationandorderingofthisandotherManningbooks,pleasevisitwww.manning.com.Thepublisheroffersdiscountsonthisbookwhenorderedinquantity.Formoreinfor

2、mation,pleasecontactSpecialSalesDepartmentManningPublicationsCo.20BaldwinRoadPOBox261ShelterIsland,NY11964Email:orders@manning.com©2012byManningPublicationsCo.Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmitted,inanyformorb

3、ymeanselectronic,mechanical,photocopying,orotherwise,withoutpriorwrittenpermissionofthepublisher.Manyofthedesignationsusedbymanufacturersandsellerstodistinguishtheirproductsareclaimedastrademarks.Wherethosedesignationsappearinthebook,andManningPublicationswasawareofa

4、trademarkclaim,thedesignationshavebeenprintedininitialcapsorallcaps.Recognizingtheimportanceofpreservingwhathasbeenwritten,itisManning’spolicytohavethebookswepublishprintedonacid-freepaper,andweexertourbesteffortstothatend.Recognizingalsoourresponsibilitytoconserveth

5、eresourcesofourplanet,Manningbooksareprintedonpaperthatisatleast15percentrecycledandprocessedwithouttheuseofelementalchlorine.ManningPublicationsCo.Developmenteditor:JeffBleiel20BaldwinRoadTechnicalproofreaders:TriciaHoffman,AlexOttPOBox261Copyeditor:LindaRecktenwald

6、ShelterIsland,NY11964Proofreader:MaureenSpencerTypesetter:GordanSalinovicCoverdesigner:MarijaTudorISBN9781617290183PrintedintheUnitedStatesofAmerica12345678910–MAL–171615141312ToJosephandMilobriefcontentsPART1CLASSIFICATION............................................

7、...................11■Machinelearningbasics32■Classifyingwithk-NearestNeighbors183■Splittingdatasetsonefeatureatatime:decisiontrees374■Classifyingwithprobabilitytheory:naïveBayes615■Logisticregression836■Supportvectormachines1017■ImprovingclassificationwiththeAdaBoos

8、tmeta-algorithm129PART2FORECASTINGNUMERICVALUESWITHREGRESSION..............1518■Predictingnumericvalues:regression1539■Tree-basedre

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