weka使用报告

1、WEKA使用实验报告一、实验目的数据挖掘是通过分析存在于数据库里的数据来解决问题。在数据挖掘中计算机以电子化的形式存储数据，并且能自动的查询数据，通过关联规则、分类与回归、聚类分析等算法对数据进行一系列的处理，寻找和描述数据里的结构模式，进而挖掘出潜在有用的信息。WEKA是一种开源的数据挖掘工具。WEKA的全名是怀卡托智能分析环境(WaikatoEnvironmentforKnowledgeAnalysis)，是-款免费的，非商业化的数据挖掘工具，其源代码可从( HYPERLINK http:/www.cs.waikato.acm http:/www.cs.waikato.acmz./ml/w

2、eka/)得到，我们在本次实验中所使用到的相关数据，也是从该处获得的。WEKA作为一个公开的数据挖掘工作平台，集合了大量能承担数据挖掘任务的机器学习算法，包括对数据进行预处理，分类，回归，聚类，关联规则以及在新的交互式界面上的可视化。本次试验，我们要通过学习WEKA工具的使用，与上课内容相结合，针对某些数据挖掘算法建立起数据挖掘模型，进而对数据分析技术有更深层次的了解。二、实验准备在启动WEKA时，会弹出GUI选择器，选择使用WEKA和数据的四种方式。如下图所示:GwckftCUEChwsflfj_ToolsclpAppLicataaits.WEKAEkpLnrMTheUmverstlynbV

3、iTG-nBIHtZrF輝4Clpj沁.FWTtEhoiflity5n5EEhtfTlLBWdWS-ltyZJSBT：ZHFGgdl-DA昭曲小1?11|耳xwtA“垃旷沢L380L的0gCo.c0.01D012222a22222333L9S*l_l,23EtOLW.D弓D.CD.aL.L1JLJa.anw?L.CflSSSC.HZSH3LK0QQO.C0.0g逊；OjTTTTTS(13333330W888Q420220D9CILO0.0305SEL5.2&3E.0BS&95LGL.OL39.DIQD.Cd.o0.KSM&a.LSIBSD.13L13LO.37ZL33323508809uO.C

4、0.0CitLllLl0畸飢(19*1445-&7G32D9:D.Oo.aO9444-M1D&2B4LT77731.3hD9251.530L97D9C0.00.0LILI07-12TIM4fl2.L3W5217.0曲1-J0.L3L11L01JL13L3LftM；30fl6?1.2l27t询90L7iD$:些0.01&S512TTTTa1.百他kLIl-B0L17.D9CD.2ZZ2770.0O0333330?Ti5Wl.QO.MM3l2I.3S-0LuQCO.CCl.flG0lTTTS0717713O3074U7L321407909CD.C0.0L277TB0S29B3L333Z0EO333

5、3L-lLJ30L25.D9C0.00.0OTJ3335n0OHJdCTO.3442!T5l5055u0cO.Co.oC-5S555E045S4IVit72223O我Lg7(X03S.D生qD.Co.aL.22Z22a.272L66i.a0.LT豹0陀2口0.CO.Q151.277詣L4L13L2.2环LSlEL0LSdQCO.C0.0S：i5bE1415?D7222Z2o型riseLT98.0LK.Di.CD.Co.a.竺竺D.H3&5D.133333r.20皿00g-jB.C0.01:0阳1.00.577n1.LE20237DgD.L1L11Lo.a22777B1143-S22fiK72.

6、5&472型LK.O2.Pi.CD.Ca.QQ.2K73-L.闹Q.2?LHS23&30Lffi0Q口O.C0.02：38ES9L4W9L4SJ3303.OW052494:LEKD9C些o.aeetidii1丐i.a0516339曲230.0LUDi.CD.Cn.o2:irras?1.39-45a15?0启09E.d0.01722221.34LS70222222QT2L3327IS0皿D9CILOo.aC-33SS3弓a01851DfilLUI.O950L9Dgc0.0013L11L3.LMflTZfl.O3.1.0545L12.038ugcO.C0.01.115bH0743L3L1JL：L4

7、6305za2K.0L33.DQ.CD.Oo.aO.KS&a.L364B3D.画SSSO.】曲彌312EZ0L45D9C0.00.0OJJ0C0.00.0217.0遜QO.Co.o.-s2Io46?264b6：2曲皿Z3LB9.0LB；.DD.CD.a1.2232L.M372.DK0Z1220幻09uO.C0.01.esj0询曲1)3SSi-0L30tigijO.C0.0G7S2220SD74D7L:SSSbG-H|f|Yh希|CCi&r-a三、实验内容1.贝叶斯算法点“Choose”按钮选择“bayes”，这是WEKA中实现的贝叶斯算法。选择Cross-Validatioinfolds=10

8、,然后点击“start”按钮，就可以生成贝叶斯模型并且该模型的误差分析结果出现在右边“Classifieroutput”中。下图是本例中训练数据集的误差分析结果：RodiDrormacionScJiHnewtJra,clnssirterfl,b3jTE3NiBLvta匹3EtelaClODf3E-3E&DZInstEnces:15D0ft.tcrltoiefl：2Dr-esLon-cezirrid-CDLrtgion-piKe1-c-dudt：shDiL-Liae-il-ejiBiT.y-5vr-Sge-rcanv-&3de-sd.h.-sdgereanir-3flr-adlncenslLy-

9、seaiLee.wred-meanZElflDLue-TTtaZlaHHareen-neajLexr-ed-raianeitbLue-TOanix-sreeD-neaiiVEloe-Mian3acurablo?i-iMa-nHoe-EteaDeLe.53Teaci5ode510-f&ldcraaa-va1ldaTlcmCla35j.fierUDdeL(fulltrai.口:Ling1set)=WaiveEay鹽Cla.ssLLezClasskinrlbuiebrlcklace卩-14|sky(a-is?CollageWSceirfiit0-15?niLDdDlrf0-14?p(iuh(0-1-

10、1gassegrelaD-ceDzrc44-CDLZIS-EDQ0.7EE7134.B37E91.3949129.d157-06212S.24Sa134.DBEEacdrEev-63r526769,2729TO,a93Efi7,2da56?r92017L.9334H.5L3Lwslhzmum20522Q22Q20i23S2D7TSE-ELSlOnLtH1.004-1.004.1.0041.0041.DD4-Srrar12zedcrDBS-ralldac丄lhiSuzmary12Lfi2540.77910.05540.225622.6144%64.5645*ai.07$LS-9333Corrc-

11、clyClassiridrngcanoesLnaarrscLLyClassilelIjisracsKappaBtatisciaMeanahs&lutserrorRootnreanaquGrrdsrrarRelariiTEatsol口匚己=rrarRc&cr-ElativeaquarederrorTc-talJlmibeioflastance1500=De匚日ilEdccuracyEyClBBS=TPRatsFPSawFreciji&nRecallE-JfeaajzeBDCAi=sCL33a0.S460,0450r75S0.546,0420r?67b匚iulcfia二匕a_99LD10.?910

12、.995LskOulSED*OL10.7360-.1SSm细0.S15SoliageCL酣4：D.0270.5440.554CL弱4o.g4GEment072LD.130.4ti50.721O.5C50.293vindjw0-945D.0D30.5S2D/45a.9630-999parK099D10.S9-9950.S99grassweightedAvg-0b1L0.03U.S32o.en0.7930.97=ConfusionBatrlx=hcdEf?-alasdifLedas194005s001a二brickfaaeD2ie0200D1b=5ky6396157a1c=toLlage17011

13、90401d-aern39c百121470D1E二windowD0490223D1f=pathan20205Ig=grassmwi同时，还可以得到校验数据集的误差结果，如下图所示:=Evalnailonont-earsec=5uircnarY=CorrectlyClassifiedInstances2417-.037ineorreDclyClassifie-dInacancesISfi22.9E3Kappacaziatic0.73LCMeanabsoluteerrur0.0659Roc-tmeanstn-arederror.Z464Relativesabsolute-error26.S912i

14、Roc-tre1azivesqjarederror70.34,31冬TotalEwnherofInatarsceaE10=EetoiL-edAccuzocyEyCLas3=TPRateFPRatePrecisionRecallF-MeasiJireROCAr-eaC13330.9520.0l0-7290.9520.532o.geebri亡rfaeeDu951010.9910.9950*999skyD.1390.0130S4CM330.23Om93Sfo-1iage0.6620.Q53o.eoe00020.6430-973匚二n亡且上D.5350.1590.40S0.5950.4340.55Ir

15、fllld&Wu.y北0,004a.9670.9360.9510.359pach0.96701D.Qfi?0.9230.994graaaWeightedivg*D.710-0410-.750*770*7130 x951=CanfuaiDDHat匚让=己cde亠j-cLasslZle-1as1190015001a=brickfaz0109L10001b=sky3DIT1101001a-foliage901970301d二aeinmt3001575001e=window00150ee01二=parti0010301191a=grass本次校验数据集的准确率为77.037%，准确率不高。2.决策树算

16、法训练数据集得到的决策树为下图所示:IgifcTrfrn筑&HiMtrsJiitCwkE.pIia?ci：vin4rinruwef.3irawsssrJi:&fe-i和咚I沖曲J-MTT7F耆注:护/0衲冲1A；brKITKi1.3iiMidIP沐H131WFp3DT*4TU!f*ld|J丫紳亍弧齐砂CHIT-iiMKnsTJii曲liWViR*1mnun茁，托怦门*打吃i*4WW2trEwtFHiy恃或ms谓-|14|忙|.PI.s.rid対v=13|-u二巧.。单“ncrrnriFH护CM呦段W*I叱D內EMIW3W3ir同时我们还可以查看误差分析:一三StzEtifirdc-iDaa-v

17、ElidaticnSumuar?Ccrrectly1133111.2a7勺KappascarlaGlc0.95D2Ite呂elatQluzeerror0.D13SRcotran耳e口曰匸皀zice-de-0L057ReLativeatsaluzeerror5647L%Rootrelative-aquuredtrz-or30,2115%ToCaLdumberofInatonceaIS00DetailelAccuracyfiyClasaIPRaceEPRawPMCiBlonRaeaLlE-N-Sdaur-eRDC型心ClassDuQ5O.OD40-575Du55fiD胡霹0.9S1brickfac

18、-E1O.OD10.9951o.se1=kyD.Q42o.oieG.E950.942D.9L60.975fnliag-eD.341O.OD90,945D,941D,3430,978ermrut0,9770,0170.B910,877DBB40,91window0.3B70.0010.99E0.9870.3910.99-7pathD.9Q1D购Lgia.saWelgti-edS.vg.D.35T0.QD70.952D.357D.95T0985=匚口rLCualcnKatrlx=ahcdef9-匚Ifissifi-=das19fiD31500110-bricjtfacr：022000a001Ibw

19、jjty012300Ia*foliaa=20目207E_C1l1亏emen匸30179aC11e-WLEldCW00a3a233011f-parh00a020205|ig-gxass校验数据集后得到结果为:=Eval口日匚landelresEme匸=Ewnnai:/CorrtGtlyClassifi-cdTastiness巧9sfi.nze%IncD-rrectl/ClassifiedLnstiaces3.S272%XanpasrarLs匚1亡0.9553Mein,absoluteerror0.0127RoorEieansquaredrror0*1005Relati7tabaDluteerror5,1771aSootralarlvesmiarederror2S-.Q507tTottalNurkrofInstanceseio=DecaLiedAccuracyCZl己日m=I?RazeEFRarerCL3LOHRaealLF-Kea.siLMROCHraaClass0.S52D,D040.976D,9?20,9540,994brickface101L11sKyQ.975D.D190.902D.9750.937O.9S6foliage0.373D.D10.3390.9730.95S