数据挖掘 随机森林

随机森林junjun2016年2月8日随机森林实例Markdown脚本及数据集：/s/1bnY6ar9实例一、用随机森林对鸢尾花数据进行分类#1、加载数据并查看data("iris")summary(iris)##Sepal.LengthSepal.WidthPetal.LengthPetal.Width##Min.:4.300Min.:2.000Min.:1.000Min.:0.100##1stQu.:5.1001stQu.:2.8001stQu.:1.6001stQu.:0.300##Median:5.800Median:3.000Median:4.350Median:1.300##Mean:5.843Mean:3.057Mean:3.758Mean:1.199##3rdQu.:6.4003rdQu.:3.3003rdQu.:5.1003rdQu.:1.800##Max.:7.900Max.:4.400Max.:6.900Max.:2.500##Species##setosa:50##versicolor:50##virginica:50######str(iris)##'data.frame':150obs.of5variables:##$Sepal.Length:num4.655.44.654.44.9...##$Sepal.Width:num3.52.93.1...##$Petal.Length:num1.5...##$Petal.Width:num0.1...##$Species:Factorw/3levels"setosa","versicolor",..:1111111111...#2、创建训练集和测试集数据set.seed(2001)library(caret)##Loadingrequiredpackage:lattice##Loadingrequiredpackage:ggplot2##Warning:package'ggplot2'wasbuiltunderRversion3.2.3index<-createDataPartition(iris$Species,p=0.7,list=F)train_iris<-iris[index,]test_iris<-iris[-index,]#3、建模library(randomForest)##randomForest4.6-12##TyperfNews()toseenewfeatures/changes/bugfixes.####Attachingpackage:'randomForest'##Thefollowingobjectismaskedfrom'package:ggplot2':####marginmodel_iris<-randomForest(Species~.,data=train_iris,ntree=50,nPerm=10,mtry=3,proximity=T,importance=T)#4、模型评估model_iris####Call:##randomForest(formula=Species~.,data=train_iris,ntree=50,nPerm=10,mtry=3,proximity=T,importance=T)##Typeofrandomforest:classification##Numberoftrees:50##No.ofvariablestriedateachsplit:3####OOBestimateoferrorrate:4.76%##Confusionmatrix:##setosaversicolorvirginicaclass.error##setosa35000.00000000##versicolor03230.08571429##virginica02330.05714286str(model_iris)##Listof19##$call:languagerandomForest(formula=Species~.,data=train_iris,ntree=50,nPerm=10,mtry=3,proximity=T,importance=T)##$type:chr"classification"##$predicted:Factorw/3levels"setosa","versicolor",..:1111111111...##..-attr(*,"names")=chr[1:105]"5""7""8""11"...##$err.rate:num[1:50,1:4]0.05130.07580.07410.04350.0505...##..-attr(*,"dimnames")=Listof2##....$:NULL##....$:chr[1:4]"OOB""setosa""versicolor""virginica"##$confusion:num[1:3,1:4]3500032203330...##..-attr(*,"dimnames")=Listof2##....$:chr[1:3]"setosa""versicolor""virginica"##....$:chr[1:4]"setosa""versicolor""virginica""class.error"##$votes:matrix[1:105,1:3]1111111111...##..-attr(*,"dimnames")=Listof2##....$:chr[1:105]"5""7""8""11"...##....$:chr[1:3]"setosa""versicolor""virginica"##..-attr(*,"class")=chr[1:2]"matrix""votes"##$oob.times:num[1:105]15232216171120201719...##$classes:chr[1:3]"setosa""versicolor""virginica"##$importance:num[1:4,1:5]000.34170.34918-0.00518...##..-attr(*,"dimnames")=Listof2##....$:chr[1:4]"Sepal.Length""Sepal.Width""Petal.Length""Petal.Width"##....$:chr[1:5]"setosa""versicolor""virginica""MeanDecreaseAccuracy"...##$importanceSD:num[1:4,1:4]000.045640.047110.00395...##..-attr(*,"dimnames")=Listof2##....$:chr[1:4]"Sepal.Length""Sepal.Width""Petal.Length""Petal.Width"##....$:chr[1:4]"setosa""versicolor""virginica""MeanDecreaseAccuracy"##$localImportance:NULL##$proximity:num[1:105,1:105]1111111111...##..-attr(*,"dimnames")=Listof2##....$:chr[1:105]"5""7""8""11"...##....$:chr[1:105]"5""7""8""11"...##$ntree:num50##$mtry:num3##$forest:Listof14##..$ndbigtree:int[1:50]1159999911119...##..$nodestatus:int[1:17,1:50]1-1111-1-11-1-1...##..$bestvar:int[1:17,1:50]4043300100...##..$treemap:int[1:17,1:2,1:50]20468001000...##..$nodepred:int[1:17,1:50]0100023032...##..$xbestsplit:num[1:17,1:50]0.801.655.254.85006.0500...##..$pid:num[1:3]111##..$cutoff:num[1:3]0.3330.3330.333##..$ncat:Namedint[1:4]1111##....-attr(*,"names")=chr[1:4]"Sepal.Length""Sepal.Width""Petal.Length""Petal.Width"##..$maxcat:int1##..$nrnodes:int17##..$ntree:num50##..$nclass:int3##..$xlevels:Listof4##....$Sepal.Length:num0##....$Sepal.Width:num0##....$Petal.Length:num0##....$Petal.Width:num0##$y:Factorw/3levels"setosa","versicolor",..:1111111111...##..-attr(*,"names")=chr[1:105]"5""7""8""11"...##$test:NULL##$inbag:NULL##$terms:Classes'terms','formula'length3Species~Sepal.Length+Sepal.Width+Petal.Length+Petal.Width##....-attr(*,"variables")=languagelist(Species,Sepal.Length,Sepal.Width,Petal.Length,Petal.Width)##....-attr(*,"factors")=int[1:5,1:4]0100000100...##......-attr(*,"dimnames")=Listof2##........$:chr[1:5]"Species""Sepal.Length""Sepal.Width""Petal.Length"...##........$:chr[1:4]"Sepal.Length""Sepal.Width""Petal.Length""Petal.Width"##....-attr(*,"term.labels")=chr[1:4]"Sepal.Length""Sepal.Width""Petal.Length""Petal.Width"##....-attr(*,"order")=int[1:4]1111##....-attr(*,"intercept")=num0##....-attr(*,"response")=int1##....-attr(*,".Environment")=<environment:R_GlobalEnv>##....-attr(*,"predvars")=languagelist(Species,Sepal.Length,Sepal.Width,Petal.Length,Petal.Width)##....-attr(*,"dataClasses")=Namedchr[1:5]"factor""numeric""numeric""numeric"...##......-attr(*,"names")=chr[1:5]"Species""Sepal.Length""Sepal.Width""Petal.Length"...##-attr(*,"class")=chr[1:2]"randomForest.formula""randomForest"pred<-predict(model_iris,train_iris)mean(pred==train_iris[,5])##[1]1#5、预测pred_iris<-predict(model_iris,test_iris)table(pred_iris,test_iris[,5])####pred_irissetosaversicolorvirginica##setosa1500##versicolor0132##virginica0213mean(pred_iris==test_iris[,5])##[1]0.9111111library(gmodels)CrossTable(pred_iris,test_iris[,5])######CellContents##|-------------------------|##|N|##|Chi-squarecontribution|##|N/RowTotal|##|N/ColTotal|##|N/TableTotal|##|-------------------------|######TotalObservationsinTable:45######|test_iris[,5]##pred_iris|setosa|versicolor|virginica|RowTotal|##-------------|------------|------------|------------|------------|##setosa|15|0|0|15|##|20.000|5.000|5.000||##|1.000|0.000|0.000|0.333|##|1.000|0.000|0.000||##|0.333|0.000|0.000||##-------------|------------|------------|------------|------------|##versicolor|0|13|2|15|##|5.000|12.800|1.800||##|0.000|0.867|0.133|0.333|##|0.000|0.867|0.133||##|0.000|0.289|0.044||##-------------|------------|------------|------------|------------|##virginica|0|2|13|15|##|5.000|1.800|12.800||##|0.000|0.133|0.867|0.333|##|0.000|0.133|0.867||##|0.000|0.044|0.289||##-------------|------------|------------|------------|------------|##ColumnTotal|15|15|15|45|##|0.333|0.333|0.333||##-------------|------------|------------|------------|------------|####实例二、用坦泰尼克号乘客是否存活数据应用到随机森林算法中在随机森林算法的函数randomForest()中有两个非常重要的参数，而这两个参数又将影响模型的准确性，它们分别是mtry和ntree。一般对mtry的选择是逐一尝试，直到找到比较理想的值，ntree的选择可通过图形大致判断模型内误差稳定时的值。randomForest包中的randomForest(formula,data,ntree,nPerm,mtry,proximity,importace)函数：随机森林分类与回归。ntree表示生成决策树的数目（不应设置太小，默认为500）；nPerm表示计算importance时的重复次数，数量大于1给出了比较稳定的估计，但不是很有效（目前只实现了回归）；mtry表示选择的分裂属性的个数；proximity表示是否生成邻近矩阵，为T表示生成邻近矩阵；importance表示输出分裂属性的重要性。下面使用坦泰尼克号乘客是否存活数据应用到随机森林算法中，看看模型的准确性如何。#1、加载数据并查看：同时读取训练样本和测试样本集train<-read.table("F:\\R\\Rworkspace\\RandomForest/train.csv",header=T,sep=",")test<-read.table("F:\\R\\Rworkspace\\RandomForest/test.csv",header=T,sep=",")#注意：训练集和测试集数据来自不同的数据集，一定要注意测试集和训练集的factor的levels相同，否则，在利用训练集训练的模型对测试集进行预测时，会报错！！！str(train)##'data.frame':891obs.of8variables:##$Survived:int0111000011...##$Pclass:int3131331332...##$Sex:Factorw/2levels"female","male":2111222211...##$Age:num2238263535NA5422714...##$SibSp:int1101000301...##$Parch:int0000000120...##$Fare:num7.2571.287.9253.18.05...##$Embarked:Factorw/4levels"","C","Q","S":4244434442...str(test)##'data.frame':418obs.of7variables:##$Pclass:int3323333233...##$Sex:Factorw/2levels"female","male":2122121212...##$Age:num34.5476227221430261821...##$SibSp:int0100100102...##$Parch:int0000100100...##$Fare:num7.8379.698.6612.29...##$Embarked:Factorw/3levels"C","Q","S":2323332313...#从上可知：训练集数据共891条记录，8个变量，Embarked因子水平为4；测试集数据共418条记录，7个变量，Embarked因子水平为3；训练集中存在缺失数据；Survived因变量为数字类型，测试集数据无因变量#2、数据清洗#1）调整测试集与训练基地因子水平levels(train$Embarked)##[1]"""C""Q""S"levels(test$Embarked)##[1]"C""Q""S"levels(test$Embarked)<-levels(train$Embarked)#2)把因变量转化为因子类型train$Survived<-as.factor(train$Survived)#3)使用rfImpute()函数补齐训练集的缺失值NAlibrary(randomForest)train_impute<-rfImpute(Survived~.,data=train)##ntreeOOB12##300:16.39%7.83%30.12%##ntreeOOB12##300:16.50%8.93%28.65%##ntreeOOB12##300:16.72%8.74%29.53%##ntreeOOB12##300:16.50%8.56%29.24%##ntreeOOB12##300:17.28%9.47%29.82%#4)补齐测试集的缺失值：对待测样本进行预测,发现待测样本中存在缺失值，这里使用多重插补法将缺失值补齐summary(test)##PclassSexAgeSibSp##Min.:1.000female:152Min.:0.17Min.:0.0000##1stQu.:1.000male:2661stQu.:21.001stQu.:0.0000##Median:3.000Median:27.00Median:0.0000##Mean:2.266Mean:30.27Mean:0.4474##3rdQu.:3.0003rdQu.:39.003rdQu.:1.0000##Max.:3.000Max.:76.00Max.:8.0000##NA's:86##ParchFareEmbarked##Min.:0.0000Min.:0.000:102##1stQu.:0.00001stQu.:7.896C:46##Median:0.0000Median:14.454Q:270##Mean:0.3923Mean:35.627S:0##3rdQu.:0.00003rdQu.:31.500##Max.:9.0000Max.:512.329##NA's:1#可是看出测试集数据存在缺失值NA,Age和Fare的数据有NA#多重插补法填充缺失值：library(mice)##Loadingrequiredpackage:Rcpp##mice2.252015-11-09imput<-mice(data=test,m=10)####iterimpvariable##11AgeFare##12AgeFare##13AgeFare##14AgeFare##15AgeFare##16AgeFare##17AgeFare##18AgeFare##19AgeFare##110AgeFare##21AgeFare##22AgeFare##23AgeFare##24AgeFare##25AgeFare##26AgeFare##27AgeFare##28AgeFare##29AgeFare##210AgeFare##31AgeFare##32AgeFare##33AgeFare##34AgeFare##35AgeFare##36AgeFare##37AgeFare##38AgeFare##39AgeFare##310AgeFare##41AgeFare##42AgeFare##43AgeFare##44AgeFare##45AgeFare##46AgeFare##47AgeFare##48AgeFare##49AgeFare##410AgeFare##51AgeFare##52AgeFare##53AgeFare##54AgeFare##55AgeFare##56AgeFare##57AgeFare##58AgeFare##59AgeFare##510AgeFareAge<-data.frame(Age=apply(imput$imp$Age,1,mean))Fare<-data.frame(Fare=apply(imput$imp$Fare,1,mean))#添加行标号：test$Id<-s(test)Age$Id<-s(Age)Fare$Id<-s(Fare)#替换缺失值：test[test$Id%in%Age$Id,'Age']<-Age$Agetest[test$Id%in%Fare$Id,'Fare']<-Fare$Faresummary(test)##PclassSexAgeSibSp##Min.:1.000female:152Min.:0.17Min.:0.0000##1stQu.:1.000male:2661stQu.:22.001stQu.:0.0000##Median:3.000Median:26.19Median:0.0000##Mean:2.266Mean:29.41Mean:0.4474##3rdQu.:3.0003rdQu.:36.653rdQu.:1.0000##Max.:3.000Max.:76.00Max.:8.0000##ParchFareEmbarkedId##Min.:0.0000Min.:0.000:102Length:418##1stQu.:0.00001stQu.:7.896C:46Class:character##Median:0.0000Median:14.454Q:270Mode:character##Mean:0.3923Mean:35.583S:0##3rdQu.:0.00003rdQu.:31.472##Max.:9.0000Max.:512.329#从上可知：测试数据集中已经没有了NA值。#3、选着随机森林的mtry和ntree值#1）选着mtry(n<-length(names(train)))##[1]8library(randomForest)for(iin1:n){model<-randomForest(Survived~.,data=train_impute,mtry=i)err<-mean(model$err.rate)print(err)}##[1]0.2100028##[1]0.1889116##[1]0.1776607##[1]0.1902606##[1]0.1960938##[1]0.1953451##[1]0.1951303##[1]0.2018745#从上可知：mtry=2或者mtry=3时，模型内评价误差最小，故确定参数mtry=2或者mtry=3#2)选着ntreeset.seed(2002)model<