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Árvore de Decisão

Árvore de Decisão. > x<-read.csv('Local onde está/casas.csv') > library(rpart) > arvore<-rpart(classifica ~ local + idade + estado+ tipo,data=x). > arvore n= 100 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 100 63 medio (0.19 0.24 0.11 0.37 0.09)

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Árvore de Decisão

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  1. Árvore de Decisão • > x<-read.csv('Local onde está/casas.csv') • > library(rpart) • > arvore<-rpart(classifica ~ local + idade + estado+ tipo,data=x)

  2. > arvore • n= 100 • node), split, n, loss, yval, (yprob) • * denotes terminal node • 1) root 100 63 medio (0.19 0.24 0.11 0.37 0.09) • 2) estado=Mbom 23 13 Mbom (0.3 0.087 0.43 0.17 0) • 4) local=bom,mau,Mmau 14 7 bom (0.5 0.14 0.14 0.21 0) * • 5) local=Mbom,medio 9 1 Mbom (0 0 0.89 0.11 0) * • Plot(arvore)

  3. >previsao<-predict(arvore,x) • bom mau Mbom medio Mmau • 1 0.00000000 0.5714286 0.00000000 0.1428571 0.2857143 • 2 0.04761905 0.2380952 0.00000000 0.5714286 0.1428571 • 3 0.04761905 0.2380952 0.00000000 0.5714286 0.1428571 • > previsao<-predict(arvore,x,type='class') • [1] mau medio medio bom medio medio medio

  4. Matriz de Confusão • > table(x$classifica,previsao) • previsao • bom mau Mbom medio Mmau • bom 15 0 0 4 0 • mau 2 12 0 10 0 • Mbom 3 0 8 0 0 • medio 7 3 1 26 0 • Mmau 0 6 0 3 0

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