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meas:²âÊÔÊý¾Ý£¬Ò»Ðдú±íÒ»¸öÑù±¾£¬Áдú±íÑù±¾ÊôÐÔ£¬N*M
species:ÿ¸öÑù±¾¶ÔÓ¦µÄÀà,N*1
kfoldLoos:½»²æÑéÖ¤:È·¶¨Ñù±¾ÑµÁ·ºóµÄÄ£Ð͵ĴíÎóÂÊ
predict:²âÊÔ¼¯¾·ÖÀàÄ£ÐÍ´¦Àíºó·Öµ½µÄÀà
knn·ÖÀàÆ÷
knn = fitcknn(meas,species,'NumNeighbors',5);
CVMdl = crossval(knn);
kloss = kfoldLoss(CVMdl);
predict(knn,ones(1,size(meas,2))) |
pca½µÎ¬£ºÖ÷³É·Ö·ÖÎö
//latent:ÌØÕ÷Öµ£¨´Ó´óµ½Ð¡),scoreÌØÕ÷ÏòÁ¿
[coeff, score, latent, tsquared, explained] =
pca(data);
//score¼´Îª´Ó´óµ½Ð¡ÅÅÐòºóµÄÌØÕ÷¾ØÕó£¬È¡Ç°kÁм´ÎªÈ¡Ñù±¾×î¾ß´ú±íÐÔµÄk¸öÊôÐÔ
//explained¼´ÎªÃ¿Ò»ÁжÔÓ¦µÄÓ°ÏìÁ¦£¬ËùÓÐÁÐ¼ÓÆðÀ´Îª100 |
bpÉñ¾ÍøÂç
ÃüÁîÐÐÊäÈënntool
svm·ÖÀàÆ÷
svm = fitcsvm(meas,species);
CVMdl = crossval(svm);
kloss = kfoldLoss(CVMdl); |
ÆÓËØ±´Ò¶Ë¹
naivebayes =
fitcnb(meas, species);
nb = crossval(naivebayes);
kloss = kfoldLoss(nb); |
¾ö²ßÊ÷cart·ÖÀàÆ÷
cart = fitctree(meas,species);
CVMdl = crossval(cart);
kloss = kfoldLoss(CVMdl); |
Ëæ»úÉÁÖ·ÖÀàÆ÷
b = TreeBagger(nTree,meas,species,'OOBPrediction','on');
rf = oobError(b);
kloss = rf(nTree,1); |
¼¯³Éѧϰ·½·¨
ada = fitensemble(meas,species,'AdaBoostM1',100,'Tree', 'Holdout',0.5);
kloss = kfoldLoss(ada,'mode','cumulative');
kloss = kloss(100,1); |
matlab»úÆ÷ѧϰ¿â
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