R语言朴素贝叶斯分类上机指导

朴素贝叶斯分类上机指导、实验目的：1.掌握矩阵数据的输入，输出，以及矩阵和数据框数据的转换，认识一下list型数据。2.理解并掌握朴素贝叶斯分类原理。3•会使用klaR包中的NaiveBayes函数实现贝叶斯分类算法。二、 实验内容：本实验利用朴素贝叶斯分类方法对课本 P144页playtennis数据集建立模型并预测。三、 实验步骤：1•将课本P144页playtennis数据以矩阵形式输入，注意理解下面红色代码。data<-matrix(c("sunny","hot","high","weak","no","sunny","hot","high","strong","no","overcast","hot","high","weak","yes","rain","mild","high","weak","yes","rain","cool","normal","weak","yes","rain","cool","normal","strong","no","overcast","cool","normal","strong","yes","sunny","mild","high","weak","no","sunny","cool","normal","weak","yes","rain","mild","normal","weak","yes","sunny","mild","normal","strong","yes","overcast","mild","high","strong","yes","overcast","hot","normal","weak","yes","rain","mild","high","strong","no"),byrow=TRUE,dimnames=list(day=c(),condition=c("outlook","temperature","humidity","wind","playtennis")),nrow=14,ncol=5);#上网查询dimnames的用法#输出一下data数据：#将矩阵转化成数据框>data1<-data.frame(data)>data1outlooktemperaturehumidity windplaytennis1sunnyhothighweakno2sunnyhothighstrongno3overcasthothighweakyes4rainmildhighweakyes5raincoolnormalweakyes6raincoolnormalstrongno7overcastcoolnormalstrongyes8sunnymildhighweakno9sunnycoolnormalweakyes10rainmildnormalweakyes11sunnymildnormalstrongyes12overcastmildhighstrongyes13overcasthotnormalweakyes14rainmildhighstrongno#将手动输入的数据保存成txt文件>write.table(data1,file="playtennis.txt",sep="")#可以重新读入保存的txt文件data2<-read.table("playtennis.txt",head=TRUE)2.理解并掌握朴素贝叶斯分类原理,读懂下列代码#算出去玩与不玩的先验概率prior.yes<-sum(data2[,5]=="yes")/length(data2[,5]);}}prior.no<-sum(data2[,5]=="no")/length(data2[,5]);#建立朴素贝叶斯分类函数bayespre<-function(condition){post.yes<-sum((data2[,1]==condition[1])&(data2[,5]=="yes"))/sum(data2[,5]=="yes")*sum((data2[,2]==condition[2])&(data2[,5]=="yes"))/sum(data2[,5]=="yes")*sum((data2[,3]==condition[3])&(data2[,5]=="yes"))/sum(data2[,5]=="yes")*sum((data2[,4]==condition[4])&(data2[,5]=="yes"))/sum(data2[,5]=="yes")*prior.yes;post.no<-sum((data2[,1]==condition[1])&(data2[,5]=="no"))/sum(data2[,5]=="no")*sum((data2[,2]==condition[2])&(data2[,5]=="no"))/sum(data2[,5]=="no")*sum((data2[,3]==condition[3])&(data2[,5]=="no"))/sum(data2[,5]=="no")*sum((data2[,4]==condition[4])&(data2[,5]=="no"))/sum(data2[,5]=="no")*prior.no;return(list(prob.yes=post.yes,prob.no=post.no,prediction=ifelse(post.yes>=post.no,"yes","no")));#利用建立的朴素贝叶斯函数做预测bayespre(c("rain","hot","high","strong"))$prob.yes[1]0.005291005$prob.no[1]0.02742857$prediction[1]"no"#这些结果是多少？用函数bayespre()算一下，你能手动算出来吗？把你的算式写出来bayespre(c("sunny","mild","normal","weak"))bayespre(c("overcast","mild","normal","weak"))bayespre(c("sunny","cool","high","strong"))3使用klaR包中的NaiveBayes函数实现贝叶斯分类算法NaiveBayes()函数的语法和参数如下：NaiveBayes(formula,data, …,subset,na.action=na.pass)NaiveBayes(x,grouping,prior,usekernel=FALSE,fL=0, •…)formula指定参与模型计算的变量，以公式形式给出，类似于 y=x1+x2+x3；data用于指定需要分析的数据对象；na.action指定缺失值的处理方法，默认情况下不将缺失值纳入模型计算，也不会发生报错信息，当设为“na.omit”时则会删除含有缺失值的样本；x指定需要处理的数据，可以是数据框形式，也可以是矩阵形式；grouping为每个观测样本指定所属类别；prior可为各个类别指定先验概率，默认情况下用各个类别的样本比例作为先验概率；usekernel指定密度估计的方法(在无法判断数据的分布时，采用密度密度估计方法) ，默认情况下使用标准的密度估计，设为 TRUE时，则使用核密度估计方法；fL指定是否进行拉普拉斯修正， 默认情况下不对数据进行修正，当数据量较小时，可以设置该参数为1，即进行拉普拉斯修正。#通过抽样建立训练样本和测试样本index<-sample(2,size=nrow(iris),replace=TRUE,prob=c(0.75,0.25))train<-iris[index==1,]test<-iris[index==2,]#加载R包并使用朴素贝叶斯算法library(MASS)library(klaR)#因子化train$Species<-as.factor(train$Species)res2<-NaiveBayes(Species~.,data=train)pre<-predict(res2,newdata=test[,1:4])#生成实际与预判交叉表和预判精度table(test$Species,pre$class)23110000920011sum(diag(table(test$Species,pre$class)))/sum(table(test$Species,pre$class))0.9375读懂上面的例子。仿照上面的例子使用klaR包中的NaiveBayes函数建立playtennis・txt贝叶斯分类模型，并预测上面的例子。c("rain","hot","high","strong")c("sunny","mild","normal","weak")c("overcast","mild","normal","weak")c("sunny","cool","high","strong")#将上面的情况建立dataframe作为测试题test<-data.frame()test<-rbind(c("rain","