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32 lines
1.0 KiB
32 lines
1.0 KiB
3 years ago
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install.packages("randomForest")
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library(randomForest)
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head(iris)
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# iris 개수만큼의 1 or 2의 70% : 30% 확률로 랜덤한 값을 생성
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idx = sample(2, nrow(iris), replace = T, prob = c(0.7, 0.3))
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trainData = iris[idx == 1,]
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testData = iris[idx == 2,]
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# 의사결정 트리 100개 생성
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model = randomForest(Species~., data=trainData, ntree=100, proximity=T)
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model
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# Call:
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# randomForest(formula = Species ~ ., data = trainData, ntree = 100, proximity = T)
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# Type of random forest: classification
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# Number of trees: 100
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# No. of variables tried at each split: 2
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# OOB estimate of error rate: 6.36%
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# Confusion matrix:
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# setosa versicolor virginica class.error
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# setosa 36 0 0 0.00000000
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# versicolor 0 34 3 0.08108108
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# virginica 0 4 33 0.10810811
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# OOB: Out Of Bag
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table(trainData$Species, predict(model))
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importance(model) # 지니계수: 값이 높은 변수가 클래스를 분류하는데 가장 큰 영향을 줌
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