ohwwlejspbfotbplistscmswaikatoacnz send wekalist mailing list submissions iuxxdkqklistscmswaikatoacnz subscribe unsubscribe via world wide web visit httpslistscmswaikatoacnzmailmanlistinfowekalist via email send message subject body help ohwwlejspbfotbplistscmswaikatoacnz reach person managing list pobypuhshvhzjlistscmswaikatoacnz replying please edit subject line specific contents wekalist digest todays topics 1 preserving order split leads inferior results harry wells 2 preserving order split leads inferior results peter reutemann 3 attribute selection test set rafiqul haque 4 distance clusters peter reutemann 5 instance weights j48 shaoyi zhang 6 instance weights j48 peter reutemann 7 select attributes cross validation thomas 8 weka help peter reutemann 9 explorer test options carmelo saffioti 10 explorer test options peter reutemann uwpglhvyahoocom sat apr 26 155013 2008 message 1 date sun 24 feb 2008 134659 0800 pst harry wells subject wekalist preserving order split leads inferior results iuxxdkqklistscmswaikatoacnz messageid 15669830posttalknabblecom contenttype textplain charsetusascii classification problem tested option preserve order split however noticed significant drop observed accuracy training set test set 10fold cross validation gave accuracy around 90 running dataset 10times using 66 training sample using different seed values gave average around 91 tested preserve order option accuracy test set dropped 75 used 2 datasets included weka distribution result results test set much inferior using preserve order option rather anything else anyone experienced problem missing something arent missing anything classifiers sensitive sequence data arrives data ordered class label use percentage split preserved order last class label underrepresented maybe represented training set thats reason one normally performs 10 runs 10fold crossvalidation order get reasonable numbers run crossvalidation data randomized nominal classes stratified get similar class distribution different folds makes sense indeed please one last question inferior results also present regression problem mean model unstable one suggesting possibly overfitting etc thank time harry view message context httpwwwnabblecompreservingorderinsplitleadstoinferiorresultswhytp15663312p15669830html sent weka mailing list archive nabblecom dlnirllcwaikatoacnz sat apr 26 155013 2008 message 2 date mon 25 feb 2008 114401 1300 peter reutemann subject wekalist preserving order split leads inferior results weka machine learning workbench list messageid 47c1f3313090501waikatoacnz contenttype textplain charsetiso88591 formatflowed arent missing anything classifiers sensitive sequence data arrives data ordered class label use percentage split preserved order last class label underrepresented maybe represented training set thats reason one normally performs 10 runs 10fold crossvalidation order get reasonable numbers run crossvalidation data randomized nominal classes stratified get similar class distribution different folds makes sense indeed please one last question inferior results also present regression problem mean model unstable one suggesting possibly overfitting etc data sorted according class attribute time numeric number eg ascending order training data split preserved order representative entire dataset chopped highest values every classificationregression scheme tries fit model onto training data training data chosen poorly likely perform poorly test data well regarding unstableoverfitting really depends scheme schemes may need see full range numeric class order build useful model others might robust hth cheers peter peter reutemann dept computer science university waikato nz httpwwwcswaikatoacnzfracpete 64 7 8384466 ext 5174 dnrxv_ttnxyahoocom sat apr 26 155013 2008 message 3 date sun 24 feb 2008 151834 0800 pst rafiqul haque subject wekalist attribute selection test set dlnirllcwaikatoacnz weka machine learning workbench list messageid 13041452330qmweb30005mailmudyahoocom contenttype textplain charsetiso88591 dear experts found problem calculate distance clusters wekanot distance instances best regards ____________________________________________________________________________________ better friend newshound knowitall yahoo mobile try httpmobileyahoocom_yltahu06i62sr8hdtdypao8wcj9tacj dlnirllcwaikatoacnz sat apr 26 155013 2008 message 4 date mon 25 feb 2008 131039 1300 peter reutemann subject wekalist distance clusters wekalist messageid 47c2077f7040205waikatoacnz contenttype textplain charsetiso88591 formatflowed please post new messages new subject wekalist read mailing list etiquette post httpwwwcswaikatoacnzmlwekamailinglist_etiquettehtml found problem calculate distance clusters wekanot distance instances theres direct way since depends cluster algorithm determine clusters simplekmeans instance calculates centroids would enable calculate distance clusterscentroids see member variable m_clustercentroids cheers peter peter reutemann dept computer science university waikato nz httpwwwcswaikatoacnzfracpete 64 7 8384466 ext 5174 ezwakhcsseunimelbeduau sat apr 26 155013 2008 message 5 date mon 25 feb 2008 134758 1000 shaoyi zhang subject wekalist instance weights j48 iuxxdkqklistscmswaikatoacnz messageid 20080225033320m94252csseunimelbeduau contenttype textplain charsetiso88591 dear weighted dataset hope use j48 classifier classification dataset weighted instances assigned classes weight class tried find doc instance weights j48 failed page found httpamathkaistackryoungikdocuweka32 1htmlwekaclassifiersj48binc45splithtmlweightswekacoreinstance reading words weightsinstance sure yet details short code sample format arff files instance weights weights weight thank dlnirllcwaikatoacnz sat apr 26 155013 2008 message 6 date mon 25 feb 2008 172932 1300 peter reutemann subject wekalist instance weights j48 weka machine learning workbench list messageid 47c2442c3040601waikatoacnz contenttype textplain charsetiso88591 formatflowed weighted dataset hope use j48 classifier classification dataset weighted instances assigned classes weight class tried find doc instance weights j48 failed ill pass one details short code sample format arff files instance weights weights weight arff format store weights use xml version xrff format developer version information xrff format see following article httpwekasourceforgenetwekadocindexphpenxrff_2835629 look wiki article add weights dataset code examples add weights dataset httpwekasourceforgenetwikiindexphpadd_weights_to_dataset hth cheers peter peter reutemann dept computer science university waikato nz httpwwwcswaikatoacnzfracpete 64 7 8384466 ext 5174 cmxlleedxbbptsuperevait sat apr 26 155013 2008 message 7 date mon 25 feb 2008 153913 0100 cet thomas subject wekalist select attributes cross validation iuxxdkqklistscmswaikatoacnz messageid 2008022514391359c2a2029e0deva232dadanetit contenttype textplain charsetiso88591 html attachment scrubbed url httpslistscmswaikatoacnzpipermailwekalistattachments20080225964539faattachment0001html dlnirllcwaikatoacnz sat apr 26 155013 2008 message 8 date tue 26 feb 2008 105329 1300 peter reutemann subject wekalist weka help weka machine learning workbench list messageid 47c338d91070002waikatoacnz contenttype textplain charsetiso88591 formatflowed made nnumber clusters dataset using weka35 n number instances dataset size dataset 144 attributes 2001 instances want know instance fall cluster weka use clusterinstanceinstance method wekaclusterersclusterer class also see section clustering following wiki article information using clusterer api httpwekasourceforgenetwikiindexphpuse_weka_in_your_java_code dont want work code level use one following filters see corresponding javadoc information wekafiltersunsupervisedattributeaddcluster wekafiltersunsupervisedattributeclustermembership hth cheers peter peter reutemann dept computer science university waikato nz httpwwwcswaikatoacnzfracpete 64 7 8384466 ext 5174 yruyieliberoit sat apr 26 155013 2008 message 9 date mon 25 feb 2008 232114 0100 carmelo saffioti subject wekalist explorer test options weka machine learning workbench list messageid 026c01c877fcbc378c006e0000c0carmelo contenttype textplain charsetiso88591 hi reading wekadoc saw models always trained whole training set question train model crossvalidation option 10 folds trained whole training set 10 subsets question test options thanking advance cheers carmelo next part html attachment scrubbed url httpslistscmswaikatoacnzpipermailwekalistattachments2008022597c24376attachment0001html abkstonehengecom sat apr 26 154855 2008 messageid 31086b240802160805r5c045116uc5ec67e5efeb4371mailgmailcom date sat 16 feb 2008 080506 0800 tom phoenix kcytkjqecbgmailcom subject excel type sorting cc soiofdvxgperlorg mimeversion 10 contenttype textplain charsetcontenttransferencoding 7bit feb 15 2008 136 pm wrote im trying figure excel type sort perl cant seem find anything specifically want goes think want sort multiple criteria nothing excel imagine 3 columns numbers b c want sort descending order starting column column b column c thing dont want order change sorting b dont want order b change sorting c sort criterion thats tie go use criterion b finally c thats single sort operation using sort sub something like sub sort_by_abc aa cmp ba ab cmp bb ac cmp bc course thats probably way criteria stored give general idea httpperldocperlorgfunctionssorthtml good luck tom phoenix stonehenge perl training llgnioiicamalacbdeio contenttype messagerfc822 contentdisposition inline filename dlnirllcwaikatoacnz sat apr 26 155013 2008 message 10 date tue 26 feb 2008 120403 1300 peter reutemann subject wekalist explorer test options weka machine learning workbench list messageid 47c349631040506waikatoacnz contenttype textplain charsetiso88591 formatflowed reading wekadoc saw models always trained whole training set model statistics output trained full training dataset informational purposes way explorer commandline work twostage process train classifier full training dataset output model explorer step suppressed via options dialog unchecking output model start scratch buildtest classifier according test options question train model crossvalidation option 10 folds trained whole training set 10 subsets statistics output based 10 traintest splits question test options see hth cheers peter peter reutemann dept computer science university waikato nz httpwwwcswaikatoacnzfracpete 64 7 8384466 ext 5174 _______________________________________________ wekalist mailing list iuxxdkqklistscmswaikatoacnz httpslistscmswaikatoacnzmailmanlistinfowekalist end wekalist digest vol 60 issue 35 thu 07 aug 2008 201403 0500 wekalist digest vol 60 issue 35