Overfitting ak Underfitting
Overfitting mooy su model bi jàppee ay done yuñ tàggat ba noppi jàllul ci misaal yu bees yi; underfitting mooy su amee lu yomb lool ngir jàpp motif bi dëgg.
Résumé
Hitting the sweet spot between them is the central challenge of machine learning.
Plongeur bu xóot
Bépp model mën na ànd ak tàggat bu am àpp, waaye mébet bi mooy ñu mëna def lu baax ci done yuñu gisul. Benn xeetu overfit dafay jàppee bruit ak quirks yi ci ensemble tàggat yaram ni dañuy nekk siñaal dëgg: mën na am 99% ci done tàggat yaram waaye daanu ba 70% ci ensemble test. Benn xeetu underfit mooy jafe-jafe bi ci beneen wàll bi, dafa dëgër lool ngir jàpp structure bi ci suuf, moo tax du def lu baax ci done tàggat ak test yépp. Diggante tàggat yaram ak def examen mooy màndarga biy wane. Underfitting dafay wane njuumte yu bari fépp (bias bu bari); overfitting dafay wane njuumte ci tàggat yaram bu néew waaye njuumte ci test bu rëy (variance bu rëy). Xam-xam bi mooy xam ban jafe-jafe nga am, ndax saafara yi dañuy jëm ci yoon yu wuute.
Gis-gis xarala
Overfitting ak underfitting ñaari njeextalu njaayum bias-variance lañu. Bias njuumte la bu bawoo ci xalaat yu yomb lool; variance njuumte la ci nekk sensible lool ci misaalu tàggat bi. Benn model ligneer bu ndaw amna biais bu rëy ak variance bu néew (ñu mënatul); xeetu model bu rëy bu amul ay tënk amna parçage bu néew ak variance bu rëy (overfits). Tolluwaayu njuumte bi ñuy seentu dafay nuru biais-carré boole ci variance boole ci bruit buñu mënul wàññi. Praktisien yi dañuy gis jafe-jafe bi suñu méngalee njubte gi ci ensemble tàggat bi ak ensemble validation biñ tëye, di xool fi ñaari courbe yi di wuute.
njeextalu pexe
dogal yu gëna leer
Daf lay jàppale nga tàqale kàddu yu leer ci wàllu xarala ak làkku fësal njaay.
Njëgg ak budget
Mën nga laaj laaj yu gëna baax ci samp gi balaa ngay dugal xaalis wala sa jotu liggéey.
Ekip ak def liggéey
Ekip yi bokk xam-xam ñoo gëna mëna jël yenn dogal ci wàllu produit, politik ak jàng.
Ëlëgu Overfitting ak Underfitting
Baatu-jàngat yooyu ñu ngi wéy di nekk fonndaasioŋ, waaye reso neuronal yu bari dañu jafeel nataal bu yàgg bi. Modèle yu bees yi mën nañu am parametre yu ëppu poñ yi waaye ba leegi dañuy generalise bu baax, regime bu yéeme bu ñuy woowe yenn saa yi 'ñaari wàcci' fu njuumti test yi wàcciwaat ginaaw peak overfitting. Gëstu dafay gëna bàyyi xelam ci li waral model yu bari parametre di yamale, cër bi regularisasioŋ bu nëbbu di def ci optimisatër yi, ak gëna mëna gis coppite ci séddale bi ci anam wu otomatik. Xaarandi diagnostic yu gëna riis yuy wane overfitting ci liggéey bi ndax done yu dëggu yi dañuy sori ci done yi ñuy tàggat.
Doxal ci àdduna dëgg
Seggal spam buy màndargaal bépp imeel bu am tur wiñ ko yónnee ndax ki ko yónnee dafa spam bu baax ci done yiñ tàggat, te amul spammer yu bees (overfitting).
Xeetu njëgu kër buy jëfandikoo meetar kaare kese te baña bàyyi xel ci barab bi, néeg yi ak anam wi ñu ko defee, moo tax dafay namm bu baax ci dëkkuwaay yu seer yi (underfitting).
Benn nataalu medsin buy jàng ngir gis filigranu scanner bu hopitaal bi ci barabu feebar bi, ba noppi mu lajj ci yeneen hopitaal yi (overfitting ci benn manndarga bu dul dëgg).
Tracé perte de formation ak perte de validation ci diiru formation bi ak taxawal su perte de validation tàmbalee yokk ci diir bi perte de formation di wéy di wàññeeku (teela jàpp overfitting).
Risk yi ak balustrade yi
Ekip yu bari mën nañu jëfandikoo benn baat ci anam wu wuute, kon teela leeral yaatuwaayam.
Benchmark yi mën nañu nuru lu am doole waaye performance yi ci àdduna bi duñu tolloo.
Bëgg kalite done ak palaŋu jàngat dafay faral di jur njariñ yu yomba dagg.
Roadmap ngir samp gi
Tàmbaleel ci joxe leeral ci làkk wu leer ci njariñ li nga soxla.
Tannal benn metric bu baax ak benn anam bu baaxul balaa ngay saytu.
Doxal ab pilote bu ndaw ak ay done yu representatif, du ab demo bu leer.
Bindal fi Overfitting ak Underfitting di jàppale ak fi pexe yu gëna yomba gëna baax.
Weyal di banneexu
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Gis bi ci topp
Tasaaro ci ginaaw
Laaj yi ñuy faral di laaj
What is Overfitting and Underfitting?
Overfitting mooy su model bi jàppee ay done yuñ tàggat ba noppi jàllul ci misaal yu bees yi; underfitting mooy su amee lu yomb lool ngir jàpp motif bi dëgg. Jafe-jafe bi gëna mag ci jàngu masin mooy xam barab bu neex bi ci seen biir.
Benn model dafay am 98% ci done yi mu tàggat waaye 71% rek lay am ci test buñ tëye. Lan mooy jafe-jafe bi gëna mëna am?
Benn bërëb bu rëy fu njubteg tàggat bi yéeg waaye njubteg test bi gëna néew mooy siñaale bu yàgg bi ci overfitting: model bi dafay méngoo ak bruit bi ci done yi ci tàggat bi moo gën motif général bi.
Ban senaario moo gëna fësal ñàkka mëna liggéey?
Benn xeetu underfit yomb na lool ngir jàpp motif bi ci suuf, moo tax du def lu baax fépp, ba ci done yiñ ko tàggate.
Ci kompromis bias-variance, variance bu rëy moo gëna méngoo ak ban njariñ?
Variance bu rëy dafay tekki ni model bi dafay soppeeku lu bari ci done yi mu gis ci tàggat yaram, te loolu dafay indi overfitting. Bias bu kawe mooy leneen, njeexte lu yomb lool.
Xeetu leble-default dafay jëfandikoo àppu ki koy sàkku rek te du bàyyi xel ci xaalis biy dugg, bor yi ak jaar-jaaru leble yi. Defa baaxul ci tàggat yaram ak ci done yu bees yépp. Loo war a def ?
Performance bu baaxul fépp dafay wane ni deful dara. Fix bi mooy yokk kàttanu model bi, lu bari ci yokk ay man-mani yuy joxe leeral, suko defee mu mëna wane dëgg-dëgg lëkkaloo gi.
Lan moo waral validasioŋ wala test buñ tëye lu am solo ngir gis overfitting?
Overfitting du luñu koy gis soo xoolee ci njubte gi ci tàggat yaram. Evaluation ci done yuñu gisul dafay wane bërëb bi nekk ci digganté xam ak généralisation dëgg.