1.用party包构建决策树
以iris数据集为例。
用ctree()建立决策树，用predict()对新数据进行预测。
训练集与测试集划分：
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> str(iris)
'data.frame': 150 obs. of 5 variables:
$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
> set.seed(1234)
> ind <- sample(2, nrow(iris), replace=TRUE, prob=c(0.7, 0.3))
> trainData <- iris[ind==1,]
> testData <- iris[ind==2,]
用默认参数来建立决策树：
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> 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$Species)
setosa versicolor virginica
setosa 40 0 0
versicolor 0 37 3
virginica 0 1 31
输出规则并绘制已构建好的决策树以便查看。
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> print(iris_ctree)
Conditional inference tree with 4 terminal nodes
Response: Species
Inputs: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width
Number of observations: 112
1) Petal.Length <= 1.9; criterion = 1, statistic = 104.643
2)* weights = 40
1) Petal.Length > 1.9
3) Petal.Width <= 1.7; criterion = 1, statistic = 48.939
4) Petal.Length <= 4.4; criterion = 0.974, statistic = 7.397
5)* weights = 21
4) Petal.Length > 4.4
6)* weights = 19
3) Petal.Width > 1.7
7)* weights = 32
> plot(iris_ctree)
> # 图略
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> plot(iris_ctree, type="simple")
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> #图略
用测试集对构建好的决策树进行测试：
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> # predict on test data
> testPred <- predict(iris_ctree, newdata = testData)
> table(testPred, testData$Species)
testPred setosa versicolor virginica
setosa 10 0 0
versicolor 0 12 2
virginica 0 0 14
几点值得注意的地方：
① ctree()不能很好地处理缺失值，含有缺失值的观测有时被划分到左子树，有时划到右子树，这是由缺失值的替代规则决定的。
② 训练集和测试集需出自同一个数据集，即它们的表结构、含有的变量要一致，无论决策树最终是否用到了全部的变量。
③ 如果训练集和测试集的分类变量的水平值不一致，对测试集的预测会识别。解决此问题的方法是根据测试集中的分类变量的水平值显式地设置训练数据。
2.用rpar包构建决策树
以bodyfat数据集为例。用rpart()构建决策树，允许选择具有最小预测误差的决策树，再使用predict()对新数据进行预测。
首先查看数据：
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> data("bodyfat", package = "TH.data")
> dim(bodyfat)
[1] 71 10
> attributes(bodyfat)
$names
[1] "age" "DEXfat" "waistcirc" "hipcirc" "elbowbreadth"
[6] "kneebreadth" "anthro3a" "anthro3b" "anthro3c" "anthro4"
$row.names
[1] "47" "48" "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
[15] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70" "71" "72" "73" "74"
[29] "75" "76" "77" "78" "79" "80" "81" "82" "83" "84" "85" "86" "87" "88"
[43] "89" "90" "91" "92" "93" "94" "95" "96" "97" "98" "99" "100" "101" "102"
[57] "103" "104" "105" "106" "107" "108" "109" "110" "111" "112" "113" "114" "115" "116"
[71] "117"
$class
[1] "data.frame"
> bodyfat[1:5,]
age DEXfat waistcirc hipcirc elbowbreadth kneebreadth anthro3a anthro3b anthro3c
47 57 41.68 100.0 112.0 7.1 9.4 4.42 4.95 4.50
48 65 43.29 99.5 116.5 6.5 8.9 4.63 5.01 4.48
49 59 35.41 96.0 108.5 6.2 8.9 4.12 4.74 4.60
50 58 22.79 72.0 96.5 6.1 9.2 4.03 4.48 3.91
51 60 36.42 89.5 100.5 7.1 10.0 4.24 4.68 4.15
anthro4
47 6.13
48 6.37
49 5.82
50 5.66
51 5.91
训练集与测试集划分，和模型训练：
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> set.seed(1234)
> ind <- sample(2, nrow(bodyfat), replace=TRUE, prob=c(0.7, 0.3))
> bodyfat.train <- bodyfat[ind==1,]
> bodyfat.test <- bodyfat[ind==2,]
> # train a decision tree
> library(rpart)
> myFormula <- DEXfat ~ age + waistcirc + hipcirc + elbowbreadth + kneebreadth
> bodyfat_rpart <- rpart(myFormula, data = bodyfat.train,
+ control = rpart.control(minsplit = 10))
> attributes(bodyfat_rpart)
$names
[1] "frame" "where" "call"
[4] "terms" "cptable" "method"
[7] "parms" "control" "functions"
[10] "numresp" "splits" "variable.importance"
[13] "y" "ordered"
$xlevels
named list()
$class
[1] "rpart"
> print(bodyfat_rpart$cptable)
CP nsplit rel error xerror xstd
1 0.67272638 0 1.00000000 1.0194546 0.18724382
2 0.09390665 1 0.32727362 0.4415438 0.10853044
3 0.06037503 2 0.23336696 0.4271241 0.09362895
4 0.03420446 3 0.17299193 0.3842206 0.09030539
5 0.01708278 4 0.13878747 0.3038187 0.07295556
6 0.01695763 5 0.12170469 0.2739808 0.06599642
7 0.01007079 6 0.10474706 0.2693702 0.06613618
8 0.01000000 7 0.09467627 0.2695358 0.06620732
> print(bodyfat_rpart)
n= 56
node), split, n, deviance, yval
* denotes terminal node
1) root 56 7265.0290000 30.94589
2) waistcirc< 88.4 31 960.5381000 22.55645
4) hipcirc< 96.25 14 222.2648000 18.41143
8) age< 60.5 9 66.8809600 16.19222 *
9) age>=60.5 5 31.2769200 22.40600 *
5) hipcirc>=96.25 17 299.6470000 25.97000
10) waistcirc< 77.75 6 30.7345500 22.32500 *
11) waistcirc>=77.75 11 145.7148000 27.95818
22) hipcirc< 99.5 3 0.2568667 23.74667 *
23) hipcirc>=99.5 8 72.2933500 29.53750 *
3) waistcirc>=88.4 25 1417.1140000 41.34880
6) waistcirc< 104.75 18 330.5792000 38.09111
12) hipcirc< 109.9 9 68.9996200 34.37556 *
13) hipcirc>=109.9 9 13.0832000 41.80667 *
7) waistcirc>=104.75 7 404.3004000 49.72571 *
绘制决策树图形：
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> plot(bodyfat_rpart)
> text(bodyfat_rpart, use.n=T)
> #图略
选择具有最小预测误差的决策树：
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> opt <- which.min(bodyfat_rpart$cptable[,"xerror"])
> cp <- bodyfat_rpart$cptable[opt, "CP"]
> bodyfat_prune <- prune(bodyfat_rpart, cp = cp)
> print(bodyfat_prune)
n= 56
node), split, n, deviance, yval
* denotes terminal node
1) root 56 7265.02900 30.94589
2) waistcirc< 88.4 31 960.53810 22.55645
4) hipcirc< 96.25 14 222.26480 18.41143
8) age< 60.5 9 66.88096 16.19222 *
9) age>=60.5 5 31.27692 22.40600 *
5) hipcirc>=96.25 17 299.64700 25.97000
10) waistcirc< 77.75 6 30.73455 22.32500 *
11) waistcirc>=77.75 11 145.71480 27.95818 *
3) waistcirc>=88.4 25 1417.11400 41.34880
6) waistcirc< 104.75 18 330.57920 38.09111
12) hipcirc< 109.9 9 68.99962 34.37556 *
13) hipcirc>=109.9 9 13.08320 41.80667 *
7) waistcirc>=104.75 7 404.30040 49.72571 *
> plot(bodyfat_prune)
> text(bodyfat_prune, use.n=T)
> #图略
用决策树模型进行预测，并与实际值进行对比。图中abline()绘制了一条对角线。一个好的预测模型，绝大多数的点应该落在对角线上或者越接近对角线越好。
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> DEXfat_pred <- predict(bodyfat_prune, newdata=bodyfat.test)
> xlim <- range(bodyfat$DEXfat)
> plot(DEXfat_pred ~ DEXfat, data=bodyfat.test, xlab="Observed",
+ ylab="Predicted", ylim=xlim, xlim=xlim)
> abline(a=0, b=1)
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> #图略
3.随机森林
以iris数据集为例。
使用randomForest()存在两个限制：第一个是该函数不能处理带有缺失值的数据，要事先对缺失值进行处理；第二是分类属性的水平划分数量最大值为32，大于32的分类属性需要事先转换。
另一种是使用party包中的cforest()，该函数没有限定分类属性的水平划分数。
训练集和测试集划分：
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> ind <- sample(2, nrow(iris), replace=TRUE, prob=c(0.7, 0.3))
> trainData <- iris[ind==1,]
> testData <- iris[ind==2,]
训练随机森林模型：
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> library(randomForest)
> rf <- randomForest(Species ~ ., data=trainData, ntree=100, proximity=TRUE)
> table(predict(rf), trainData$Species)
setosa versicolor virginica
setosa 36 0 0
versicolor 0 31 1
virginica 0 1 35
> print(rf)
Call:
randomForest(formula = Species ~ ., data = trainData, ntree = 100, proximity = TRUE)
Type of random forest: classification
Number of trees: 100
No. of variables tried at each split: 2
OOB estimate of error rate: 1.92%
Confusion matrix:
setosa versicolor virginica class.error
setosa 36 0 0 0.00000000
versicolor 0 31 1 0.03125000
virginica 0 1 35 0.02777778
> attributes(rf)
$names
[1] "call" "type" "predicted" "err.rate"
[5] "confusion" "votes" "oob.times" "classes"
[9] "importance" "importanceSD" "localImportance" "proximity"
[13] "ntree" "mtry" "forest" "y"
[17] "test" "inbag" "terms"
$class
[1] "randomForest.formula" "randomForest"
根据生成的随机森林中不同的树来绘制误差率：
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> plot(rf)
> #图略
查看变量重要性：
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> importance(rf)
MeanDecreaseGini
Sepal.Length 6.485090
Sepal.Width 1.380624
Petal.Length 32.498074
Petal.Width 28.250058
> varImpPlot(rf)
> #图略
最后使用测试集进行测试，用table()和margin()查看结果。图中数据点的边距为正确分类的比例减去被归到其他类别的最大比例。一般来说，边距为正数说明该数据点划分正确。
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> irisPred <- predict(rf, newdata=testData)
> table(irisPred, testData$Species)
irisPred setosa versicolor virginica
setosa 14 0 0
versicolor 0 17 3
virginica 0 1 11
> plot(margin(rf, testData$Species))
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> #图略
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