R语言实现决策树
1.准备数据
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>install.packages("tree")
>library(tree)
>library(ISLR)
>attach(Carseats)
>High=ifelse(Sales<=8,"No","Yes") //set high values by sales data to calssify
>Carseats=data.frame(Carseats,High) //include the high data into the data source
>fix(Carseats)
2.生成决策树
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>tree.carseats=tree(High~.-Sales,Carseats)
>summary(tree.carseats)
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//output training error is 9%
Classification tree:
tree(formula = High ~ . - Sales, data = Carseats)
Variables actually used in tree construction:
[1] "ShelveLoc" "Price" "Income" "CompPrice" "Population"
[6] "Advertising" "Age" "US"
Number of terminal nodes: 27
Residual mean deviance: 0.4575 = 170.7 / 373
Misclassification error rate: 0.09 = 36 / 400
3. 显示决策树
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>plot(tree . carseats )
>text(tree .carseats ,pretty =0)
4.Test Error
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//prepare train data and test data
//We begin by using the sample() function to split the set of observations sample() into two halves, by selecting a random subset of 200 observations out of the original 400 observations.
>set . seed (1)
>train=sample(1:nrow(Carseats),200)
>Carseats.test=Carseats[-train,]
>High.test=High[-train]
//get the tree model with train data
>tree. carseats =tree (High~.-Sales , Carseats , subset =train )
//get the test error with tree model, train data and predict method
//predict is a generic function for predictions from the results of various model fitting functions.
>tree.pred = predict ( tree.carseats , Carseats .test ,type =" class ")
>table ( tree.pred ,High. test)
High. test
tree. pred No Yes
No 86 27
Yes 30 57
>(86+57) /200
[1] 0.715
5.决策树剪枝
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/**
Next, we consider whether pruning the tree might lead to improved results. The function cv.tree() performs cross-validation in order to cv.tree() determine the optimal level of tree complexitycost complexity pruning is used in order to select a sequence of trees for consideration.
For regression trees, only the default, deviance, is accepted. For classification trees, the default is deviance and the alternative is misclass (number of misclassifications or total loss).
We use the argument FUN=prune.misclass in order to indicate that we want the classification error rate to guide the cross-validation and pruning process, rather than the default for the cv.tree() function, which is deviance.
If the tree is regression tree,
>plot(cv. boston$size ,cv. boston$dev ,type=’b ’)
*/
>set . seed (3)
>cv. carseats =cv. tree(tree .carseats ,FUN = prune . misclass ,K=10)
//The cv.tree() function reports the number of terminal nodes of each tree considered (size) as well as the corresponding error rate(dev) and the value of the cost-complexity parameter used (k, which corresponds to α.
>names (cv. carseats )
[1] " size" "dev " "k" " method "
>cv. carseats
$size //the number of terminal nodes of each tree considered
[1] 19 17 14 13 9 7 3 2 1
$dev //the corresponding error rate
[1] 55 55 53 52 50 56 69 65 80
$k // the value of the cost-complexity parameter used
[1] -Inf 0.0000000 0.6666667 1.0000000 1.7500000
2.0000000 4.2500000
[8] 5.0000000 23.0000000
$method //miscalss for classification tree
[1] " misclass "
attr (," class ")
[1] " prune " "tree. sequence "
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//plot the error rate with tree node size to see whcih node size is best
>plot(cv. carseats$size ,cv. carseats$dev ,type=’b ’)
/**
Note that, despite the name, dev corresponds to the cross-validation error rate in this instance. The tree with 9 terminal nodes results in the lowest cross-validation error rate, with 50 cross-validation errors. We plot the error rate as a function of both size and k.
*/
>prune . carseats = prune . misclass ( tree. carseats , best =9)
>plot( prune . carseats )
>text( prune .carseats , pretty =0)
//get test error again to see whether the this pruned tree perform on the test data set
>tree.pred = predict ( prune . carseats , Carseats .test , type =" class ")
>table ( tree.pred ,High. test)
High. test
tree. pred No Yes
No 94 24
Yes 22 60
>(94+60) /200
[1] 0.77
输入数据做数据曲线拟合，当然该有数据，本经验从以如下数据作为案例。添加数据到curvefitting程序这一步就是将你要拟合的数据添加到curvefitting程序中，同时给你拟合的曲线命名。选择曲线拟合的方法类型常见的拟合曲线有多项式的、指数的、对数的等等。curvefitting程序提供了很多的方法。你可以根据自己的数据具体选择。选择好方法后，按照提供的公式选择具体的选项本文的数据近似为线性的，我们选择多项式拟合的一阶方法。拟合结果查看拟合后，curvefitting会给你具体的函数表达式，你可以将他给出的参数的值带入你选择的方法中。结果说明在结果中，不仅可以看到函数的表达式，同时他还给出了95%置信区间的参数值，以及拟合好坏的一些指标，如：SSE:R-square:AdjustedR-square:RMSE:画出图像虽然在curvefitting程序有自带的图像显示，但是一般最好将拟合结果显示到单独的图像窗口。保存结果曲线拟合结束后，你可以保存你的拟合结果。选择你保存的路径即可。
