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利用party來做決策樹分類

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數據：iris data

目標：

利用Sepal.Length, Sepal.Width,Petal.Length and Petal.Width 來預測 Species of ?owers.

預處理：

分成訓練，測試樣本集：

> set.seed(1234)
> ind <- sample(2, nrow(iris), replace=TRUE, prob=c(0.7, 0.3))
> trainData <- iris[ind==1,]
> testData <- iris[ind==2,]

接下里就是

1.Load package party,

2.build a decision tree,

3.and check the prediction.

> library(party)
> myFormula <- Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width
> iris_ctree <- ctree(myFormula, data=trainData)
> # check the prediction
> table(predict(iris_ctree), trainData$Species)

?? ? ? ? ? ? ? ? ? ? ? ?setosa ? ? ? ? versicolor ? ? ? ? ?virginica
setosa ? ? ? ? ? ? ? ? ? ? 40 ? ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ? 0
versicolor ? ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ?37 ? ? ? ? ? ? ? ? ? ?3
virginica ? ? ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ?1 ? ? ? ? ? ? ? ? ? 31

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下面主要是分析結果：即分析得到的決策樹

> ?print(iris_ctree)

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> ?plot(iris_ctree)

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> ?plot(iris_ctree, ?type="simple")

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在測試樣本上使用決策樹得到預測結果

> # predict on test data
> testPred <- predict(iris_ctree, newdata = testData)
> table(testPred, testData$Species)
?testPred ? ? ? ? ? ?setosa ? ? ? ?versicolor ? ? ? ? ?virginica
setosa ? ? ? ? ? ? ? ? ? ? 10 ? ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ?0
versicolor ? ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? 12 ? ? ? ? ? ? ? ? ? ?2
virginica ? ? ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ?14

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注意的問題：

The current version of ctree (i.e. version 0.9-9995) does not handle missing values well. An
instance with a missing value may sometimes go to the left sub-tree and sometimes to the right.

Another issue is that, when a variable exists in training data and is fed into ctree but does not
appear in the built decision tree, the test data must also have that variable to make prediction.
Otherwise, a call to predict would fail. Moreover, if the value levels of a categorical variable
in test data are di?erent from that in train data, it would also fail to make prediction on the
test data. One way to get around the above issue is, after building a decision tree, to call ctree
build a new decision tree with data containing only those variables existing in the ?rst tree, and
to explicitly set the levels of categorical variables in test data to the levels of the corresponding
variables in train data.

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4.2 ? ?Building Decision Trees with Package rpart

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未完待續

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4.3 ? ?Random Forest

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Package randomForest 中的?cforest可以來構建?Random Forest進行預測

第一步：The iris data is split below into two subsets: training (70%) and testing (30%).

> ind <- sample(2, nrow(iris), replace=TRUE, prob=c(0.7, 0.3))
> trainData <- iris[ind==1,]
> testData <- iris[ind==2,]

第二步：

Load randomForest and then train a random forest.
> library(randomForest)
> rf <- randomForest(Species ~ ., data=trainData, ntree=100, proximity=TRUE)
> table(predict(rf), trainData$Species)

?? ? ? ? ? ? ? ? ? ? ?setosa ? ? ? ? versicolor ? ? ? ? ? virginica
setosa ? ? ? ? ? ? ? ? ? 38 ? ? ? ? ? ? ? ? ? ?0 ? ? ? ? ? ? ? ? ? ?0
versicolor ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ?33 ? ? ? ? ? ? ? ? ? ?2
virginica ? ? ? ? ? ? ? ? ? 0 ? ? ? ? ? ? ? ? ? ?2 ? ? ? ? ? ? ? ? ?28

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后面未完待續

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