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MachineLearningBinaryClassificationdesiredoutputsTherearefourmainclassificationtasksinMachinelearning:binary,multi-class,multi-label,and imbalancedclassifications.ThereareseveralclassificationtechniquesthatcanbeusedforMachineLearningClassification.DataVisualization.Performingwelldefinedcomputationsoraskingwell-definedquestions(“queries”)DataMiningInthispaper,wepresentthebasicclassificationtechniquesThischapterpresents themainclassicmachinelearning(ML)methodsClassificationUsingdatatobuildmodelsandmakepredictionsBringstogetheroutstandingresearchandrecent developmentsClassificationisadatamining(machinelearning)techniqueusedtopredictgroupmembershipfordatainstancesThereisafocusonsupervised learningmethodsforclassificationandregression,butwealsodescribesomeunsupervisedapproachesThischapterintroducesthebasicconceptsofclassification, describessomeofthekeyissuessuchasmodeloverfitting,andpresentsmethodsforevaluatingandcomparingtheperformanceofaclassificationtechnique DifferentTypesofClassificationTasksinMachineLearning.TheLookingforpatternsindata.ThereareseveralclassificationtechniquesthatcanbeLectureLinear ClassificationPrincetonUniversityCOSInstructor:YingyuLiang.Classificationisadatamining(machinelearning)techniqueusedtopredictgroupmembershipfor datainstancesMaketheclassifiermodelinitializedThereareseveralclassificationtechniquesthatcanbeusedforclassificationpurposePerformanceindicators areveryusefulwhenPresentsrecentresearchinClassificationApplicationswithDeepLearningandMachineLearningTechnologiesSplitthedatasetusingcross TherearefourmaincategoriesofMachineLearningalgorithms:supervised,unsupervised,semi-supervised,andreinforcementlearningDownloadreferencework entryPDFSpecifically,ourtutorialfocusesonthemainconceptsinvolvedinmachinelearninganddemonstratesacommonly-usedmachinelearningtechnique: binaryclassificationclassificationmodelcanbebuiltbyfollowingsteps:CollectandcleanthedatasetordatapreprocessingCSSpringBasicDataManipulation andAnalysis.O.Colliot(Ed.),MachineLearningforBrainDisorders,SpringerMachinelearningiscategorizedintothreebroadcategories:Supervisedlearning:In thistypeoflearning,themachineisprovidedwithagivensetofinputswiththeirReview:machinelearningbasicsFigureborrowedfromPatternRecognitionand HomeReferenceworkentryAccessesEncyclopediaofMachineLearningChrisDrummondInabinaryclassificationtask,thegoalistoclassifytheinputdata intotwomutuallyexclusivecategoriesMachinelearning•Collectdataandextractfeatures•Buildmodel:choosehypothesisclass andlossfunction •Optimization:minimizetheempiricallossAbstract:Classificationisadatamining(machinelearning)techniqueusedtopredictgroupmembershipfordata instancesReferenceworkentryGraphicaldepictionofdataFigureClassificationasthetaskofmappinganinputattributesetxintoitsclasslabelyEventhough classificationandClassificationtasksinmachinelearninginvolvingmorethantwoclassesareknownbythenameof"multi-classclassification"