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This version: 4/4/17 4:37 PM A guided tour to Machine Learning using MATLAB® © Oge Marques, PhD – 2016-2017 Introduction • This document guides you through several tutorials, papers, and resources related to Machine Learning (with emphasis on image and vision tasks) using MATLAB. • It assumes no prior exposure to Machine Learning or MATLAB. • It is structured as a step-by-step guide. It is best that you follow it in the intended sequence. Part 1- Accessing MATLAB You are expected to have frequent access to a computer running MATLAB and some of its toolboxes (notably, the Image Processing, Computer Vision System, Statistics and Machine Learning, Neural Network, and Fuzzy Logic toolboxes) for your assignments and projects. Here are some options to consider: 1. Purchase your own copy of the student version of MATLAB. It costs $99, is fully functional, and comes with several toolboxes. For more details, go to: http://www.mathworks.com/academia/student_version/ 2. Access via the Engineering Cloud (https://tsg.eng.fau.edu/software/matlab/). Instructions on how to access the Engineering Cloud are posted here: https://tsg.eng.fau.edu/software/vmware-remote-desktop-access/ Once connected, select the "All Engineering Students" pool of windows desktops. Once logged in to windows, click on Start -> All Programs -> MATLAB 2016a -> MATLAB 2016a. Be sure to save any files to your Z: drive. If you have issues connecting or starting MATLAB, please contact [email protected]. 3. Consider a free alternative to MATLAB. The most popular is GNU Octave (http://www.gnu.org/software/octave/), which has been used by many Machine Learning professors, students, and researchers. They are "work in progress" and are not 100% compatible with MATLAB. So please use them at your own risk.
Transcript of A guided tour to Machine Learning using MATLAB® › wp-content › uploads › 2020 › ... ·...
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AguidedtourtoMachineLearningusingMATLAB®
©OgeMarques,PhD–2016-2017
Introduction
• Thisdocumentguidesyouthroughseveraltutorials,papers,andresourcesrelatedtoMachineLearning(withemphasisonimageandvisiontasks)usingMATLAB.
• ItassumesnopriorexposuretoMachineLearningorMATLAB.• Itisstructuredasastep-by-stepguide.Itisbestthatyoufollowitintheintended
sequence.
Part1-AccessingMATLABYouareexpectedtohavefrequentaccesstoacomputerrunningMATLABandsomeofitstoolboxes(notably,theImageProcessing,ComputerVisionSystem,StatisticsandMachineLearning,NeuralNetwork,andFuzzyLogictoolboxes)foryourassignmentsandprojects.
Herearesomeoptionstoconsider:
1. PurchaseyourowncopyofthestudentversionofMATLAB.Itcosts$99,isfullyfunctional,andcomeswithseveraltoolboxes.Formoredetails,goto:http://www.mathworks.com/academia/student_version/
2. AccessviatheEngineeringCloud(https://tsg.eng.fau.edu/software/matlab/).InstructionsonhowtoaccesstheEngineeringCloudarepostedhere:https://tsg.eng.fau.edu/software/vmware-remote-desktop-access/Onceconnected,selectthe"AllEngineeringStudents"poolofwindowsdesktops.Onceloggedintowindows,clickonStart->AllPrograms->MATLAB2016a->MATLAB2016a.BesuretosaveanyfilestoyourZ:drive.IfyouhaveissuesconnectingorstartingMATLAB,[email protected].
3. ConsiderafreealternativetoMATLAB.ThemostpopularisGNUOctave(http://www.gnu.org/software/octave/),whichhasbeenusedbymanyMachineLearningprofessors,students,andresearchers.Theyare"workinprogress"andarenot100%compatiblewithMATLAB.Sopleaseusethematyourownrisk.
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Part2-LearningthebasicsofMATLAB1. TaketheMATLABOnramptrainingavailableat
https://matlabacademy.mathworks.com/R2016a/portal.html?course=gettingstarted(seeseparatedocumentforstep-by-stepinstructions).
2. (OPTIONAL)Watchthe46-min"IntroductiontoMATLAB"video:www.mathworks.com/videos/introduction-to-matlab-81592.htmlDon'tforgettodownloadtheassociatedsourcecode:http://www.mathworks.com/matlabcentral/fileexchange/49570-introduction-to-matlab--february-2015-
Part3-LearningthebasicsofMachineLearninginMATLAB1. Readthe"IntroducingMachineLearning"e-book(availableonCanvas).
2. Read"SupervisedLearningWorkflowandAlgorithms"
https://www.mathworks.com/help/stats/supervised-learning-machine-learning-workflow-and-algorithms.html
3. (OPTIONAL)Watchthe35-min"MachineLearningMadeEasy"video:www.mathworks.com/videos/machine-learning-with-matlab-100694.htmlDon'tforgettodownloadtheassociatedsourcecode:http://www.mathworks.com/matlabcentral/fileexchange/50232-machine-learning-made-easy
4. (OPTIONAL)Watchthe41-min"MachineLearningwithMATLAB"video:http://www.mathworks.com/videos/machine-learning-with-matlab-81984.htmlDon'tforgettodownloadtheassociatedsourcecode:http://www.mathworks.com/matlabcentral/fileexchange/42744-machine-learning-with-matlab
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Part4-ClassificationusingdecisiontreesinMATLABInspiredbythestepsathttps://www.mathworks.com/help/stats/classification-trees-and-regression-trees.html
1. RuntheexamplefiledtIntro.m,payingattentiontothefollowingaspects:a. Howtoloadadataset(inthiscase,it'salreadyavailablein.matformat)b. Howtocreateadecisiontree,viewit,anduseittomakeapredictionusingunseen
datac. Howtocomputeresubstitutionerroroftheresultingclassificationtreed. Howtocomputecross-validationaccuracye. Howtoselecttheappropriatetreedepthf. Howtoprunethetree
2. RuntheexamplefiledtIris.m,payingattentiontothefollowingaspects:
a. Howtoloadadataset(inthiscase,it'salreadyavailablein.matformat)b. Howtoplotdifferentviewsofthedataset(wheneverfeasible)inordertobetter
understandthedatac. Howtocreateadecisiontree,viewit,anduseittomakeapredictionusingunseen
datad. Howtocomputeresubstitutionerroroftheresultingclassificationtreee. Howtocomputecross-validationaccuracy
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Part5-LinearRegressioninMATLAB1. Runtheexamplesinthe'Stanford'subfolder.TheyarefromAndrewNg's"Machine
Learning"course(MOOC)–StanfordUniversity–Fall2011.a. ex1.mshowslinearregressionforonevariableb. ex1_multi.mshowslinearregressionwithmultiplevariables.Italsointroduces
featurenormalizationandthenormalequationmethod(analternativetogradientdescent)
2. RuntheexampleIntroToLinearRegression.minthe'Mathworks'subfolder.a. Itisbasedonhttps://www.mathworks.com/help/matlab/data_analysis/linear-
regression.html.Itbuildsandcomparestwosimplelinearregressionmodelsandintroducesthecoefficientofdetermination.
3. Runtheexamplesinthe'Regression_Demos'subfolder.Theyarealsoavailableat:http://www.mathworks.com/matlabcentral/fileexchange/35789-new-regression-capabilities-in-r2012a
a. (OPTIONAL,butrecommended)Watchtheassociatedwebinar/video:https://www.mathworks.com/videos/regression-analysis-with-matlab-new-statistics-toolbox-capabilities-in-r2012a-81869.html
b. Exploretheexamplesfollowingthissequence:StraightLine.m,CurvesSurfaces.m,andNonLinear.m.(SkiptheHousing.m,Model.mandtheGLMs.mexamples)
c. Don’tbeintimidatedordiscouragedbytherichamountofinformationavailableinsomeMATLABobjects,e.g.,LinearModel.
Part6-LogisticRegressioninMATLAB1. Runtheexamplesinthe'Stanford'subfolder.TheyarefromAndrewNg's"Machine
Learning"course(MOOC)–StanfordUniversity–Fall2011.a. ex2.mshowslogisticregressionb. ex2_reg.mshowstheuseofadditionalpolynomialfeaturesandtheimpactof
regularizationonlogisticregressionwithmultiplevariables.Don'tforgettochangethevalueoftheregularizationparameter,lambda,inline90,andrunthatsectioneverytimeyoudoso.Noticehowtheresultingdecisionboundarychangesasaresultofchangesinlambda.
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Part7-TheClassificationLearnerAppGoal:LearnhowtousetheMATLABClassificationLearnerApptoperform3-classclassificationontheFisher’sIrisdataset.1.Dataset:
Inthisexample,wewillusetheFisher’sIrisdataset.ThisisasampledatasetincludedintheMATLABStatisticsandMachineLearningToolbox.Youcanfindallsampledatasetsat:https://www.mathworks.com/help/stats/_bq9uxn4.html
2.Viewthedataset:*Thisstepisnotnecessary,justtogiveanideahowthisdatasetlookslikeToloadadatasetintotheMATLABworkspace,type:loadfilenameInthisparticularexample,wewilltypeinCommandWindow:loadfisheriris.matYoucanviewthedatasetsloadedtotheworkspacebydoubleclickingthematrixnameunderWorkspacewindow.(Pleasenoticethatyoumayhaveadifferentwindowlayoutthanthescreenshotbelow)Inthisexample,measisa150*4doublematrix.Thereare150rowseachrowrepresentsoneinstance.Thereare4columnsstoreattributeinformation(col1:sepallengthincm;col2:sepalwidthincm;col3:petallengthincm;col4:petalwidthincm).Theclassforeachinstanceisstoredinaseparate150*1cellcalled“species”.Inthiscase,thefirst50instancesbelongtoclassSetosa,thefollowing50belongtoclassVersicolorandthelast50belongtoclassVirginica.
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3.Preparethedata.Weneedtofirstloadthefisheririsdatasetandcreateatableofmeasurementpredictors(orfeatures)usingvariablesfromthedatasettouseforaclassification.TypethefollowingcommandaftertheCommandWindowprompt:
fishertable=readtable('fisheriris.csv');4.StarttheClassificationLearnerApp.Therearetwowaysofdoingthis:a)MATLABToolstrip:OntheAPPStab,underMath,StatisticsandOptimization,clicktheappicon(seescreenshotbelow).b)MATLABcommandprompt:typeclassificationLearner
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5.OntheClassificationLearnertab,intheFilesection,clickNewSession.(seescreenshotbelow)
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6.IntheNewSessiondialogbox,selectthetablefishertablefromtheworkspacelist.Note:Ifyoudidoptionalstep2,youmayfindmeasinthedialogaswell;makesurethefishertableisselected.Observethattheapphasselectedresponseandpredictorvariablesbasedontheirdatatype.Petalandsepallengthandwidtharepredictors,andspeciesistheresponsethatyouwanttoclassify.Forthisexample,donotchangetheselections.
7.Acceptthedefaultvalidationoption(5-foldcross-validation)andcontinuebyclickingStartSession.Youwillseethesessionlikefollowingscreenshot.
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8.Chooseaclassificationmodel.Inthiscase,weshalluseasimpledecisiontree.Tocreateaclassificationtreemodel,ontheClassificationLearnertab,intheClassifiersection,clickthedownarrowtoexpandthegalleryandclickSimpleTree.Thendisable
the'UseParallel'button(ifit'ssettoON)andclickTrain.
9.Examineresults
TheSimpleTreemodelisnowintheHistorylist.ThemodelvalidationscoreisintheAccuracybox.Thisnumbermaybeslightlydifferentinyourcase.Examinethescatterplot.AnXindicatesmisclassifiedpoints.Thebluepoints(setosaspecies)areallcorrectlyclassified,butsomeoftheothertwospeciesaremisclassified.UnderPlot,switchbetweentheDataandModelPredictionsoptions.Observethecoloroftheincorrect(X)points.Alternatively,whileplottingmodelpredictions,toviewonlytheincorrectpoints,cleartheCorrectcheckbox.OntheClassificationLearnertab,inthePlotssection,clickConfusionMatrixorROCCurvetogenerateConfusionMatrixorROCCurve,respectively.Eachplotwillopenonaseparatetab.Seerepresentativescreenshotsonthenextpage.ExperimentwithchangingthesettingsineachPlotsectiontofullyexaminehowthecurrentlyselectedclassifierperformedineachclass.
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10.Chooseanothermodel.
Youcantraindifferentmodelstocomparetothedecisiontree,bychoosingothermodelsintheClassifiersection.Inthisexample,IchoseFineKNN1.ClickFineKNN,andthenclickTrain.Aftertraining,youcanseetheFineKNNintheHistorylist.YoucanclickeachmodelintheHistorylisttoviewandcomparetheresults.Theaccuracyvaluemaybeslightlydifferentinyourcase.
1 Technically, you don't know what a kNN classifier is, since we haven't covered it in class (yet). But that's on purpose! My goal is to show that you can pick other classifiers, train them, and 'play' with their parameters rather easily, even if you don't quite know what is "inside the box".
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11.Tryusingdifferentattributes
Totrytoimprovethemodel,tryusingdifferentfeaturesinthemodel.Seeifyoucanimprovethemodelbyremovingfeatureswithlowpredictivepower.OntheClassificationLearnertab,intheFeaturessection,clickFeatureSelection.Youcanremoveafeaturebyunchecktheboxbesideit.
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AfterperformingFeatureSelection,anewmodelwillappearontheleft-handsideoftheapp.Youshouldthentrainitandcomparetheaccuracyresults(aswellasconfusionmatrix,ROCcurve,AUC,etc.)againstthepreviouslytrainedmodels.MATLABwillindicatethebestmodelsofarbyhighlightingthehighestaccuracyvalues.Seescreenshotbelow(obtainedaftertrying5variantsofdecisiontreesand2variantsofkNN).
12.Advancedclassifieroptions
Tolearnaboutmodelsettings,chooseamodelintheHistorylistandviewtheadvancedsettings.TheoptionsintheClassifiergalleryarepresetstartingpoints,andyoucanchangefurthersettings.OntheClassificationLearnertab,intheTrainingsection,clickAdvanced.Fordecisiontrees,considerchangingtheMaximumNumberofSplitssetting(whichcontrolstreedepth),thentrainanewmodelbyclickingTrain.ViewthesettingsfortheselectedtrainedmodelintheCurrentmodelpane,orintheAdvanceddialogbox.
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13.ExporttrainedmodelToexportthebesttrainedmodeltotheworkspace,ontheClassificationLearnertab,intheExportsection,clickExportModel.IntheExportModeldialogbox,clickOKtoacceptthedefaultvariablenametrainedClassifierorchangetoanothername.
IftheexportedmodelisadecisiontreecalledtrainedTreeClassifier,usethefollowinglineonMATLABcommandwindowtoviewtheresultingmodel(tree):view(trainedTreeClassifier.ClassificationTree,'mode','graph'); Youcanusetheexportedclassifiertomakepredictionsonnewdata.Forexample,tomakepredictionsforthefishertabledatainyourworkspace,enter:yfit = trainedClassifier.predictFcn(fishertable)
Theoutputyfitcontainsaclasspredictionforeachdatapoint.SeetheCommandWindowscreenshotbelow:
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14.GeneratecodeIfyouwanttoautomatetrainingthesameclassifierwithnewdata,orlearnhowtoprogrammaticallytrainclassifiers,youcangeneratecodefromtheapp.Togeneratecodeforthebesttrainedmodel,ontheClassificationLearnertab,intheExportsection,clickExportModel>GenerateCode.TheappgeneratescodefromyourmodelanddisplaysthefileintheMATLABEditor.SeescreenshotbelowforthegeneratedcodeinEditor:
Mathworksprovidemanynicedetailedexamples,alternatively,youcanrefertotheselinks:https://www.mathworks.com/help/stats/train-decision-trees-in-classification-learner-app.htmlhttp://www.mathworks.com/help/stats/train-logistic-regression-classifiers-in-classification-learner-app.html
