http://scikit-learn.org/stable/modules/naive_bayes.html의 예제는 Multinomial Naive Bayes 분류자를 사용하여 최상의 대상 태그를 출력합니다.sklearn naive bayes classifier에서 nbest 예측을 얻는 방법은 무엇입니까? -python
sklearn
classifier.fit()
함수에서 nbest 결과와 해당 확률을 얻으려면 어떻게해야합니까?
나는 이것을 시도하고 그것은 단지 최고 최선의 목표 태그 제공 : 내가 해봤
from sklearn.naive_bayes import MultinomialNB
from sklearn import datasets
iris = datasets.load_iris()
mnb = MultinomialNB()
y_pred = mnb.fit(iris.data, iris.target).predict(iris.data)
print y_pred
print "Number of mislabeled points : %d" % (iris.target != y_pred).sum()
을이 :
mnb.fit(iris.data, iris.target).predict_proba(iris.data)
하지만 nbest하지만 처럼 보이는 뭔가를 출력 첫 번째 열이 0이고 두 번째 열이 1이고 세 번째 열이 2 인 태그 순서입니까? 그렇다면 피팅하기 전에 내 iris.data
및 iris.target
을 정렬해야합니까?
[[ 0.75203199 0.16090571 0.08706229]
[ 0.68449076 0.19961428 0.11589496]
[ 0.71655395 0.18031248 0.10313357]
[ 0.66789673 0.20853796 0.12356531]
[ 0.75923862 0.15641199 0.08434939]
[ 0.71232842 0.18631933 0.10135225]
[ 0.69283061 0.19413936 0.11303003]
[ 0.72230231 0.1784813 0.09921639]
[ 0.64908034 0.21795677 0.13296289]
[ 0.71016301 0.18515188 0.10468511]
[ 0.7706877 0.15071277 0.07859954]
[ 0.69872772 0.19198923 0.10928305]
[ 0.70928234 0.18501507 0.10570259]
[ 0.73423744 0.16829862 0.09746394]
[ 0.84434639 0.10536809 0.05028552]
[ 0.80368141 0.13133624 0.06498235]
[ 0.76946455 0.15105144 0.07948401]
[ 0.7255851 0.17657409 0.09784082]
[ 0.73970806 0.17070375 0.08958819]
[ 0.74545506 0.16522966 0.08931528]
[ 0.70811072 0.18854972 0.10333956]
[ 0.70648697 0.18818499 0.10532804]
[ 0.7969896 0.13199963 0.07101077]
[ 0.58777843 0.25618981 0.15603175]
[ 0.64951166 0.22132827 0.12916007]
[ 0.65586031 0.21701444 0.12712524]
[ 0.647116 0.22224047 0.13064353]
[ 0.74167829 0.16758828 0.09073344]
[ 0.74468171 0.16548141 0.08983688]
[ 0.66886376 0.20873516 0.12240109]
[ 0.66014838 0.21396507 0.12588655]
[ 0.68161377 0.20339924 0.11498699]
[ 0.82411012 0.11766438 0.0582255 ]
[ 0.83205074 0.11304195 0.05490731]
[ 0.71016301 0.18515188 0.10468511]
[ 0.74308517 0.1652924 0.09162243]
[ 0.77963634 0.14494298 0.07542068]
[ 0.71016301 0.18515188 0.10468511]
[ 0.67897883 0.20084314 0.12017803]
[ 0.72628321 0.17640557 0.09731122]
[ 0.73635517 0.16968093 0.09396391]
[ 0.55384513 0.26927796 0.17687691]
[ 0.70420272 0.18658505 0.10921223]
[ 0.59635556 0.25075085 0.15289358]
[ 0.65439735 0.21981277 0.12578988]
[ 0.64951064 0.21917004 0.13131933]
[ 0.75711005 0.1585194 0.08437054]
[ 0.69691278 0.19168277 0.11140445]
[ 0.76716311 0.15262537 0.08021152]
[ 0.72544435 0.17629024 0.09826541]
[ 0.05616304 0.51141127 0.4324257 ]
[ 0.05045622 0.50190524 0.44763854]
[ 0.03940412 0.50636507 0.4542308 ]
[ 0.04710523 0.48480988 0.46808489]
[ 0.03801645 0.49775392 0.46422963]
[ 0.04556145 0.49250736 0.4619312 ]
[ 0.03966245 0.49873204 0.46160551]
[ 0.10701789 0.47535669 0.41762542]
[ 0.0536651 0.50530072 0.44103419]
[ 0.05251353 0.48336562 0.46412085]
[ 0.07633356 0.47948527 0.44418117]
[ 0.05056292 0.49342004 0.45601704]
[ 0.07291747 0.49453526 0.43254727]
[ 0.03955223 0.49594088 0.46450688]
[ 0.08936249 0.48762276 0.42301475]
[ 0.06195976 0.50612926 0.43191098]
[ 0.03831622 0.48825946 0.47342431]
[ 0.0863508 0.49450781 0.41914139]
[ 0.02709303 0.48373491 0.48917206]
[ 0.07519686 0.49028178 0.43452135]
[ 0.02440995 0.4828392 0.49275084]
[ 0.07042286 0.49642936 0.43314778]
[ 0.02455796 0.48683709 0.48860495]
[ 0.04880578 0.49984347 0.45135075]
[ 0.06389132 0.50205774 0.43405094]
[ 0.05742052 0.50419335 0.43838613]
[ 0.03993759 0.50401099 0.45605142]
[ 0.02528131 0.49378132 0.48093736]
[ 0.03918748 0.49255696 0.46825556]
[ 0.1206129 0.48274379 0.39664332]
[ 0.0748217 0.48808052 0.43709778]
[ 0.09117508 0.48671672 0.4221082 ]
[ 0.07675253 0.49259019 0.43065728]
[ 0.01952175 0.47919808 0.50128017]
[ 0.0367986 0.48532662 0.47787478]
[ 0.04575051 0.49665456 0.45759493]
[ 0.04377224 0.50435344 0.45187432]
[ 0.04137786 0.49450702 0.46411511]
[ 0.06664472 0.49270322 0.44065206]
[ 0.05283801 0.4871102 0.46005178]
[ 0.04794017 0.4900128 0.46204703]
[ 0.04505918 0.49770389 0.45723693]
[ 0.0676616 0.49295412 0.43938428]
[ 0.1033085 0.47658754 0.42010396]
[ 0.05234803 0.49047033 0.45718164]
[ 0.07213391 0.49516342 0.43270267]
[ 0.0598313 0.49357345 0.44659525]
[ 0.06145081 0.49977467 0.43877452]
[ 0.12643795 0.47099626 0.40256579]
[ 0.06070342 0.4924782 0.44681837]
[ 0.0042955 0.43723717 0.55846733]
[ 0..46132786 0.52633063]
[ 0.00804196 0.46804151 0.52391653]
[ 0.01220374 0.4719262 0.51587006]
[ 0.00663872 0.45347587 0.53988541]
[ 0.00526335 0.46448324 0.53025341]
[ 0.01877963 0.45982004 0.52140033]
[ 0.0088868 0.47816164 0.51295156]
[ 0.00897388 0.46607468 0.52495144]
[ 0.00578841 0.45971014 0.53450145]
[ 0.01677457 0.48014283 0.50308259]
[ 0.01204406 0.46873836 0.51921758]
[ 0.01020869 0.46951911 0.5202722 ]
[ 0.0100408 0.45127554 0.53868367]
[ 0.00649696 0.43748008 0.55602296]
[ 0.00930427 0.46015047 0.53054526]
[ 0.01456352 0.47933745 0.50609903]
[ 0.00702627 0.47944937 0.51352435]
[ 0.00253012 0.4388473 0.55862257]
[ 0.01793058 0.47433015 0.50773927]
[ 0.00764407 0.46221754 0.53013839]
[ 0.01270343 0.45832067 0.52897589]
[ 0.00508153 0.46485081 0.53006765]
[ 0.01829021 0.47785801 0.50385178]
[ 0.01030084 0.47164088 0.51805829]
[ 0.01305643 0.48854867 0.4983949 ]
[ 0.02048499 0.47963663 0.49987838]
[ 0.02097735 0.48087632 0.49814633]
[ 0.00771013 0.45561321 0.53667666]
[ 0.01775039 0.49657519 0.48567443]
[ 0.00863886 0.47567646 0.51568469]
[ 0.01213462 0.49867869 0.48918669]
[ 0.00669855 0.45021395 0.5430875 ]
[ 0.02528409 0.49048657 0.48422934]
[ 0.01705235 0.48108164 0.50186601]
[ 0.00592661 0.46457057 0.52950282]
[ 0.00712125 0.45269329 0.54018547]
[ 0.01514248 0.47987549 0.50498204]
[ 0.02213426 0.48045724 0.4974085 ]
[ 0.0119365 0.47535905 0.51270445]
[ 0.00646304 0.45242581 0.54111115]
[ 0.01131574 0.4700604 0.51862386]
[ 0..46132786 0.52633063]
[ 0.00643277 0.45678109 0.53678614]
[ 0.00587581 0.45021579 0.5439084 ]
[ 0.00947358 0.46237113 0.52815529]
[ 0.01310372 0.46704681 0.51984947]
[ 0.01380969 0.47411641 0.5120739 ]
[ 0.00933287 0.45973346 0.53093367]
[ 0.01733739 0.4748122 0.50785041]]
'= numpy.unique 태그 (iris.target)' –
OOO'numpy' 고유 (SECURITY) 기능을 비활성화를 갖는다() – alvas