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WekaAntonn PavelkaPraktick pouit
Weka
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Weka - vodsystm pro analzu dat a prediktivn modelovnUniversity
of Waikato, Nov Zland1993 TCL/TK, C, Makefiles1997 rozhodnut pejt
na istou JavuintegrovnaRapidMinerPetaho (systm business
intelligence)GNU General Public License
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Weka
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Ovldngrafick rozhranExplorer jednotliv innosti na
kliknutExperimenter systematick srovnnKnowledge flow innosti jako
tokpkazov dekJava API*
Weka
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Ukzka grafick rozhran ...*
Weka
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... pkazov dek ...java classpath weka.jar
weka.classifiers.bayes.NaiveBayes t data/iris.arff*
Weka
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... Java API*
Weka
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1. Attribute-Relation File Format (ARFF)*ARFF soubor
@relation spambase% spam, non-spam@attribute word_freq_make
real@attribute 'char_freq_# real@attribute {spam,
ham}@data0,0.64,0.64,spam0.21,0.28,0.5,spam0.06,0,0.71,ham
Chybjc hodnoty4.4,?,1.5,?,Tolkienetzce@attribute LCC
string@attribute LCSH string
@dataAG5, 'Encyclopedias and dictionaries.;Twentieth
century.as@ATTRIBUTE timestamp DATE "yyyy-MM-dd HH:mm:ss" @DATA
"2001-04-03 12:12:12" "2001-05-03 12:59:55"dk formt0, X, 0, Y,
"class A" {1 X, 3 Y, 4 "class A"}0, 0, W, 0, "class B" {2 W, 4
"class B"}
Weka
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2. Pedzpracovn dat*
Weka
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Histogramy*uiten seln atributpodezel seln atributbinrn clov
atribut20-hodnotov atribut
Weka
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Filtry*Remove V R 1-5,8 (V = inverze, zachovej pouze tyto
atributy)Discretizenkter algoritmy nepracuj s slyurychlennkdy i
zven pesnostipevzorkovndoplnn chybjcch atribut, odstrann chybjcch
hodnotObfuscatorPrincipal Component Analysis, Partial Least
SquaresAttributeSelection
Weka
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StringToWordVector*@attribute text string@attribute class
{class1,class2,class3}
@data'\n\t\n\t\tDumbek\'s Rand'
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Klasifikace algoritmy 1*NaiveBayes, BayesNet, Averaged
One-Dependence Estimators (AODE)SMO, SMOreg, LibSVM
Weka
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StringKernel*@attribute name string@attribute class {female,
male}@dataMidori,femaleKoichi,male
291 enskch a 385 muskch jmen (odstranno 13 univerzlnch jmen)prvn
sputn: Q2 = 63 %
Weka
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Dal SVM parametry a jejich
optimalizace*meta.CVParameterSelection P "C 0.5 50000.0 5.0" ...
Cross-validation Parameter: '-C' ranged from 0.5 to 50000.0 with
5.0 stepsClassifier Options: -C 12500.375 ...
bez predikce spolehlivosti
TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0.887
0.192 0.777 0.887 0.828 0.847 female 0.808 0.113 0.904 0.808 0.853
0.847 maleWeighted Avg. 0.842 0.147 0.849 0.842 0.842 0.847
predikce spolehlivosti logistickou regres
TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0.835
0.148 0.81 0.835 0.822 0.921 female 0.852 0.165 0.872 0.852 0.862
0.921 maleWeighted Avg. 0.845 0.158 0.846 0.845 0.845 0.921
Weka
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Predikovan spolehlivost a ROC kivka*
Weka
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Klasifikace algoritmy 2*MultilayerPerceptronvalidan
mnoinapomalLinearRegressionPLSClassifier Partial Least Squares
regressionstromyJ48, RandomForest, ...metaboosting, bagging,
...ClassifivationViaRegressionAttributeSelectedClassifierCostSensitiveClassifier
Weka
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Ven chyb*TP Rate 0.81 0.915
meta.CostSensitiveClassifier% Rows Columns2 2% Matrix elements0
21 0
cena za patn klasifikovan P je 2x vt ne za N
Weka
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Vbr atribut*Metoda
hodnocenatributChiSquaredAttributeEvalSVMAttributeEvalpodmnoinyCfsSubsetEvalWrapperSubsetEval,
ClassifierSubsetEval
Metoda prohledvnpro atributyRankerpro
podmnoinyBestFirstGeneticSearch
Redukce dimenz filtremPrincipal Component Analysis, Partial
Least Squares
Weka
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Experimenter*
Weka
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Knowledge Flow*
Weka
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Zdroje*
KnihyWEKA Manual for Version 3-7-0Data Mining: Practical Machine
Learning Tools and Techniques
Webhttp://www.cs.waikato.ac.nz/ml/weka/http://weka.wikispaces.com/http://wekadocs.com/http://www.hakank.org/weka/
Weka
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Sputn Wekyssh X lethemodule add javavytvote si pracovn adres
(mkdir , cd )wget loschmidt.chemi.muni.cz/~tonda/w.zipunzip
w.zipjava Xmx256m jar weka.jar*
Weka
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kol 1Explorer J48 a SMOspuste 2x Weku a Explorerv obou otevete
spambase.arff a bte do tabu Classifyv Test options, More options
nastavte Output predictions na Plain textv prvnm vyberte
klasifiktor trees.J48kliknte do polka vpravo od tlatka Choose a
nastavteuseLaplace: Truespuste 10-ti nsobn kov ovenv druhmvyberte
klasifiktor functions.SMOkliknte do polka vpravo od tlatka Choose a
nastavtebuildLogisticModels: TruenumFolds: 10spuste 10-ti nsobn kov
ovenSrovnejte rychlost a pesnost obou algoritm. Odhadnte uitenost
predikce dvryhodnosti vsledku (=== Predictions on test data ===,
sloupec prediction).
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Weka
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kol 2Knoledge Flow - ROC kivkyspuste Knowledge Flowotevete
spam_roc.kfnastavte ArffLoader na spambase.arffkliknte pravm
tlatkem na ArffLoader, Start loadingsrovnejte ROC kivky NaiveBayese
a BayesNetu (klik pravm tlatkem na horn Model Performance Chart,
Show chart)srovnejte ROC kivky BayesNetu a AODE. Po kliknut na bod
kivky se zobraz sla. Kolik procent spamu identifikujeme, pokud jsme
ochotn tolerovat, e ve spamovm koi skon 4 % hamu (spam = class 1,
osa X: FPR = FP/N, osa Y: TPR = TP / P)?
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Weka
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kol 3Experimenter - srovnn klasifiktorspuse Experimenterkliknte
na tlatko NewResult destination: nastavte cestu a zvolte jmno novho
ARFF souborupidejte dataset spam_discretized.arffpidejte algoritmy
bayes.AODE, tree.J48, tree.RandomForestspuste vpoet v tabu
Runjakmile skon, pejte do tabu Analysekliknte na Experiment a
Perform TestJe pesnost nkter z metod na tto sad statisticky vznamn
lep na hladin 0.05? Jak je to s Area_under_ROC?*
Weka
*/celkem/celkem*/celkem/celkem
