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finished hw3
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hw3/.Rhistory
@ -1,426 +1,79 @@
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auto = read.table("auto.data",header=T,na.strings="?")
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length(x=auto$mpg)
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glm
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glm.pred
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help(rep)
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glm.pred=rep(FALSE,397)
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glm.pred
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medium(auto$mpg)
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median(auto$mpg)
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glm.pred[auto$mpg>median(auto$mpg)]=T
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glm.pred
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contour(auto)
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contour(glm.pred ~ auto$mpg)
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contour(glm.pred,auto$mpg)
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help(contour)
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contour(auto$mpg,auto$horsepower,glm.pred)
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glm.pred
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length(glm.pred)
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table(glm.pred,auto$mpg)
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table(glm.pred,auto$mpg,auto$horsepower)
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glm.pred=rep(0,397)
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glm.pred[auto$mpg>median(auto$mpg)]=1
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glm.pred
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auto$mpg01=rep(0,397)
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auto$mpg01[auto$mpg>median(auto$mpg)]=1
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auto$mpg01
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auto$mpg01
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auto$mpg01
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plots(auto)
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plot(auto)
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boxplot(auto)
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boxplot.matrix(auto)
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help(boxplot)
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boxplot(auto$mpg01,auto)
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boxplot(auto$mpg,auto)
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boxplot(auto$mpg)
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boxplot(auto)
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boxplot(mpg01 ~ auto)
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boxplot(mpg01 ~)
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boxplot(auto$mpg01 ~ auto)
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attach(auto)
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boxplot(mpg01)
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boxplot(mpg01 ~ auto)
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boxplot(mpg01 ~ auto,auto)
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boxplot(mpg01 ~ auto,data = auto)
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help(plot.table)
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plot.table(auto)
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help(plot.table)
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plot(auto)
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plot(auto,t="box")
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help(plot.table)
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help(plot.table,plot.frame=1)
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help(plot.table)
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help(plot.table,frame.plot=1)
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help(plot.table)
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help(plot.table,frame.plot=is.num)
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help(plot.table)
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plot(auto,t="box",frame.plot=1)
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plot(auto,frame.plot=1)
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plot(auto,frame.plot=1)
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plot(auto,frame.plot=is.num)
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plot(auto,frame.plot=0)
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plot(auto,frame.plot="0")
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plot(auto,frame.plot="1")
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plot(auto,frame.plot=TRUE)
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plot(auto,frame.plot=FALSE)
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plot(auto,frame.plot=TRUE)
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plot(auto,frame.plot=T)
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plot(auto,frame.plot=1)
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boxplot(mpg~mpg01,auto)
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boxplot(mpg01 ~ mpg,auto)
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boxplot(mpg01 ~ *,auto)
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boxplot(mpg01 ~ ,auto)
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boxplot(mpg01 ~ auto,auto)
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boxplot(mpg01,auto)
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boxplot(auto)
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boxplot(auto,y=mpg01)
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boxplot(auto,y=mpg)
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boxplot(data = auto)
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boxplot(auto)
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help(for)
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plot(auto,frame.plot=1)
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plot(auto)
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names(auto)
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auto$name
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help(sample)
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x <- 1:12
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x
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sample(x)
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help(sample)
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sample(x,replace=T)
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sample(x,replace=T)
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sample(x,replace=F)
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c
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x
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sample(x,replace=T)
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x
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help(sample)
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sample(x[x>9])
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sample(x[x>8])
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help(sample)
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x <- 1:10
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sample(x[x>8])
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sample(x[x>])
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help(sample)
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help(sample)
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help(sample)
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sample(auto,size=length(mpg01)/2)
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x <- length(mpg01)
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sample(x,size=length(mpg01)/2)
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auto[sample(x,size=length(mpg01)/2)]
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auto$mpg[sample(x,size=length(mpg01)/2)]
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help(data.frame)
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data.frame(
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help(data.frame)
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auto[sample(x,size=length(mpg01)/2)]
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train = sample(x,size=length(mpg01)/2)
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train =
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auto[train]
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auto$mpg[train]
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auto$mpg[train,]
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auto$mpg[train]
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auto$mpg[23]
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auto$mpg[228]
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auto$mpg[391]
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auto.test=auto[!train]
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auto.train=auto[train]
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auto.test
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summary(auto.test)
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train=(mpg<15)
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train
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train = (sample(x,size=length(mpg01)/2))
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train
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head(auto)
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auto[,train[
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auto[,train]
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train
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help(contains)
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auto[1,train]
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train
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auto[[,train]]
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auto[[1,train]]
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autoi
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head(auto)
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head(auto[sample(nrow(auto),397/2)])
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head(auto[sample(nrow(auto),3)])
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data = data.frame(auto)
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data
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head(data[sample(nrow(data),3)])
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nrow(data)
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head(data[sample(ncol(data),3)])
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head(data[sample(ncol(data),397/2)])
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head(data[sample(ncol(data),3)])
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head(data[sample(ncol(data),3)])
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head(data[sample(ncol(data),3)])
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head(data[sample(ncol(data),3)])
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head(data[,sample(ncol(data),3)])
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head(data[,sample(ncol(data),3)])
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head(data[,sample(ncol(data),3)])
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head(data[,sample(ncol(data),3)])
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head(data[,sample(ncol(data),3)])
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head(data[sample(ncol(data),3),])
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head(data[sample(ncol(data),3),])
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head(data[sample(ncol(data),3),])
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head(data[sample(nrow(data),3),])
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head(data[sample(nrow(data),397/2),])
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head(data[sample(nrow(data),397/2),])
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head(data[sample(nrow(data),397/2),])
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head(data[sample(nrow(data),397/2),])
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head(data[sample(nrow(data),397/2),])
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head(auto[sample(nrow(auto),397/2),])
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head(auto[sample(nrow(auto),397/2),])
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head(auto[sample(nrow(auto),397/2),])
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head(auto[sample(nrow(auto),397/2),])
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head(auto[sample(nrow(auto),397/2),])
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head(auto[sample(nrow(auto),397/2),])
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head(auto[sample(nrow(auto),397/2),])
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train = auto[sample(nrow(auto),397/2),]
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[sample(nrow(auto),397/2),]
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sample(nrow(auto),397/2)
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train sample(nrow(auto),397/2)
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train = sample(nrow(auto),397/2)
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autp[train,]
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auto[train,]
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train = sample(nrow(auto),397/2)
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head(auto[train,])
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head(auto[!train,])
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traindata = auto[train,]
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testdata = auto[!train,]
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testdata
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traindata
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length(traindata)
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length(traindata$mpg)
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198*2
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summary(testdata)
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testdata = auto[!train]
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testdata
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testdata = auto[!train,]
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train
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summary(train)
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names(train)
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head(traindata)
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testdata = auto[!train,]
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testdata
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!train
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train
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?sample
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sort(train)
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train_vals = train
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train = rep(false,397)
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train = rep(F,397)
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train
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help for
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?for
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?for
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help)for)
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help(for)
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help(for)
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help lapply()
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?lapply
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sapply(train,
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?sapply
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sapply(train,
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?sapply
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train[train_vals]=T
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train
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traindata = auto[train,]
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traindata
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length(auto)
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length(traindata)
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length(traindata$mpg)
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testdata=auto[!train,]
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length(testdate$mpg)
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length(testdata$mpg)
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training_indices = sample(nrow(auto),397/2)
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train_bools = rep(F,length(auto$mpg))
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train_bools[training_indices]=T
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head(train_bools)
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length(train_bools)
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library(ISLR)
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library(MASS)
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library(class)
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train_bools <- (auto$year %% 2 == 0)
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train_data = auto[train_bools,]
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test_data = auto[!train_bools,]
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summary(train_data)
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summary(test_data)
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lda.fit
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library(MASS)
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lda.fit
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lda()
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detach(auto)
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mpg01
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mpg
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attach(test_data)
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mpg01
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names()
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names(test_data)
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ldf.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data)
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detach(test_data)
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ldf.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data)
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lda.fit
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lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data)
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lda.fit
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summary(lda.fit)
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coefficients(lda.fit)
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plot(lda.fit)
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lda.pred=predict(lda.fit,test_data)
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lda.pred=predict(lda.fit, !training_bools)
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lda.pred=predict(lda.fit, !training_indices)
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test_data
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lda.pred=predict(lda.fit, test_data)
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lda.pred
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plot(lda.pred)
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names(lda.pred)
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lda.class=lda.pres$class
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lda.class=lda.pred$class
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table(lda.class,testdata)
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table(lda.class,test_data)
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length(lda.class)
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length(test_data)
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table(lda.class,test_data$mpg01)
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mean(lda.class==test_data$mpg01)
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sum(lda.pred$posterior[,1]>=.5)
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sum(lda.pred$posterior[,1]<.5)
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lda.pred$posterior[,1]
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sum(lda.pred$posterior<.5)
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lda.pred$posterior
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lda.pred$posterior<5
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lda.pred$posterior<.5
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sum(lda.pred$posterior<.5)
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sum(lda.pred$posterior<.5[,1])
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sum(lda.pred$posterior<.5[1])
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sum(lda.pred$posterior<.5[2])
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lda.pred$posterior<.5[2]
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lda.pred$posterior<.5
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lda.pred$posterior
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lda.pred$posterior[,1]
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lda.pred$posterior[1,]
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lda.pred$posterior[,2]
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lda.pred$posterior[,1]
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lda.pred$posterior[,1]>.5
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sum(lda.pred$posterior[,1]>.5)
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sum.bool(lda.pred$posterior[,1]>.5)
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?sum
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sum.bool(lda.pred$posterior[,1]>.5,na.rm=T)
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sum(lda.pred$posterior[,1]>.5,na.rm=T)
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sum(lda.pred$posterior[,1]>.5)
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sum(lda.pred$posterior[,1]>.5,na.rm=T)
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sum(lda.pred$posterior[,1]>=.5,na.rm=T)
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sum(lda.pred$posterior[,1]<.5,na.rm=T)
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mean(lda.pred$[,1]==test_data,na.rm=T)
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lda.pred
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lda.pred$class
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lda.pred$class==test_data$mpg01
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mean(lda.pred$class==test_data$mpg01,na.rm=T)
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mean(lda.pred$class!=test_data$mpg01,na.rm=T)
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lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
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lda.fit
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mean(lda.pred$class==test_data$mpg01,na.rm=T)
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lda.pred=predict(lda.fit, test_data)
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mean(lda.pred$class==test_data$mpg01,na.rm=T)
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mean(lda.pred$class!=test_data$mpg01,na.rm=T)
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train_data == test_data
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train_data$mpg01 == test_data$mpg01
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lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
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lda.pred=predict(lda.fit, test_data)
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mean(lda.pred$class!=test_data$mpg01,na.rm=T)
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lda.pred
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lda.pred$posterior[,1]
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summary(lda.fit)
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lda.fit
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lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data)
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lda.fit
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mean(lda.pred$class!=test_data$mpg01,na.rm=T)
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lda.pred=predict(lda.fit, test_data)
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mean(lda.pred$class!=test_data$mpg01,na.rm=T)
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head(lda.pred)
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lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
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lda.pred=predict(lda.fit, test_data)
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head(lda.pred)
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mean(lda.pred$class!=test_data$mpg01,na.rm=T)
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qda.fit=qda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
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qda.fit
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qda.class=predict(qda.fit,test_data)$class
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qda.class=predict(qda.fit,test_data,na.rm=T)$class
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qda.class=predict(qda.fit,test_data)$class
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qda.class
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mean(qda.pred$class!=test_data$mpg01,na.rm=T)
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qda.pred=predict(qda.fit,test_data)
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qda.pred=predict(qda.fit,test_data,na.rm=T)
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mean(qda.pred$class!=test_data$mpg01,na.rm=T)
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glm.fit=glm(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data,family=binomial)
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glm.probs=predict(glm.fit,test_data,type="response")
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glm.pred=rep(0,199)
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glm.pred[glm.probs>.5]=1
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table(glm.pred,test_data$mpg01)
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mean(glm.pred!=test_data$mpg01)
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library(class)
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?cbind
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help(knn)
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help(knn)
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train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
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test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
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train
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test
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?knn
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knn.fit = knn(train_data,test_data,auto$mpg01[training_indices])
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knn.fit = knn(train_data,test_data,auto$mpg01[training_indices],k=1)
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knn.fit = knn(train_data,test_data,auto$mpg01[training_indices],k=1)
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?knn
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training_indices
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train_bools
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knn.fit = knn(train_data,test_data,auto$mpg01[train_bools],k=1)
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sdf = (mpg01<1)
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sdf = (auto$mpg01<1)
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sdf
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train_bools
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cbind(horsepower,displacement)
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cbind(train_data$horsepower,displacement)
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cbind(train_data$horsepower,train_data$displacement)
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cbind(auto$horsepower,auto$displacement)[train_bools]
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cbind(auto$horsepower,auto$displacement)[train_bools,]
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cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
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cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
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train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
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test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
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train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
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test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
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train.mpg01 = auto[train_bools]
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train.mpg01 = auto$mpg01[train_bools]
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test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
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train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
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test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
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train.mpg01 = auto$mpg01[train_bools]
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set.seed(56)
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knn.pred = knn(train.X,test.X,train.mpg01,k=1)
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?cbind
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?Knn
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?knn
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train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
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test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
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train.mpg01 = auto$mpg01[train_bools]
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train.X = train.X[!is.na(train.X)]
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test.X = data.frame(test.X,
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train.mpg01 = train.mpg01[!is.na(train.mpg01)]
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knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
length(train.mpg01)
|
||||
length(test.X)
|
||||
text.X
|
||||
test.X
|
||||
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
|
||||
length(test.X)
|
||||
test.X
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
|
||||
cl
|
||||
length(cl)
|
||||
length(train)
|
||||
nrows(train)
|
||||
nrow(train)
|
||||
train.X
|
||||
train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
|
||||
train.X
|
||||
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
|
||||
test.X
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
?knn
|
||||
length(train.X)
|
||||
length(train.X[1,])
|
||||
length(train.X[,1])
|
||||
?knn
|
||||
plot(auto)
|
||||
train.X = cbind(auto$horsepower,auto$displacement)[train_bools,]
|
||||
test.X = cbind(auto$horsepower,auto$displacement)[!train_bools,]
|
||||
train.mpg01 = auto$mpg01[train_bools]
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
train.X
|
||||
test.X
|
||||
train.mpg01 = auto$mpg01[train_bools]
|
||||
train.mpg01
|
||||
length(train.mpg01)
|
||||
nrow(train.X)
|
||||
knn(train.X,train.Y,train.mpg01,K=1)
|
||||
knn(train.X,train.Y,train.mpg01,k=1)
|
||||
knn(train.X,test.X,train.mpg01,k=1)
|
||||
train.X
|
||||
na.omit(train.X)
|
||||
?na.omit
|
||||
na.omit(train.X)
|
||||
na.omit(train.X)
|
||||
knn(na.omit(train.X),test.X,train.mpg01,k=1)
|
||||
knn(na.omit(train.X),test.X,na.omit(train.mpg01),k=1)
|
||||
knn(na.omit(train.X),na.omit(test.X),na.omit(train.mpg01),k=1)
|
||||
train.mpg012 = na.omit(auto$mpg01)[train_bools]
|
||||
train.mpg012
|
||||
train.mpg01
|
||||
nrow(train)
|
||||
na.omit(auto)
|
||||
auto
|
||||
na.omit(auto)
|
||||
summary(auto)
|
||||
summary(na.omit(auto))
|
||||
Auto = na.omit(auto)
|
||||
auto = na.omit(auto)
|
||||
ncol(auto)
|
||||
nrow(auto)
|
||||
auto <- na.omit(auto)
|
||||
train_bools <- (auto$year %% 2 == 0)
|
||||
train_data = auto[train_bools,]
|
||||
test_data = auto[!train_bools,]
|
||||
train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
|
||||
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
|
||||
train.mpg01 = auto$mpg01[train_bools]
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
||||
mean(knn.pred != auto$mpg01)
|
||||
mean(knn.pred != test_data$mpg01)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=2)
|
||||
mean(knn.pred != test_data$mpg01)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=3)
|
||||
mean(knn.pred != test_data$mpg01)
|
||||
knn.pred = knn(train.X,test.X,train.mpg01,k=4)
|
||||
mean(knn.pred != test_data$mpg0)
|
||||
knn.pred
|
||||
length(knn.pred)
|
||||
dim(knn.pred)
|
||||
length(test_data)
|
||||
ncol(test_data)
|
||||
nrow(test_data)
|
||||
q()
|
||||
|
||||
111
hw3/answers
111
hw3/answers
@ -85,6 +85,7 @@ Part B: Choose one of Questions 10 or 11
|
||||
given car gets high or low gas mileage based on the Auto data
|
||||
set.
|
||||
|
||||
──────────────────────────────────────────────────────────────────────────
|
||||
(a) Create a binary variable, mpg01 , that contains a 1 if mpg
|
||||
contains a value above its median, and a 0 if mpg contains a
|
||||
value below its median. You can compute the median using the
|
||||
@ -92,6 +93,9 @@ Part B: Choose one of Questions 10 or 11
|
||||
data.frame() function to create a single data set containing
|
||||
both mpg01 and the other Auto variables.
|
||||
|
||||
> auto$mpg01=rep(0,397)
|
||||
> auto$mpg01[auto$mpg>median(auto$mpg)]=1
|
||||
|
||||
> auto$mpg01
|
||||
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0
|
||||
[38] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
|
||||
@ -106,6 +110,7 @@ Part B: Choose one of Questions 10 or 11
|
||||
[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1
|
||||
|
||||
|
||||
──────────────────────────────────────────────────────────────────────────
|
||||
(b) Explore the data graphically in order to investigate the
|
||||
associ- ation between mpg01 and the other features. Which of the
|
||||
other features seem most likely to be useful in predicting mpg01
|
||||
@ -119,6 +124,16 @@ Part B: Choose one of Questions 10 or 11
|
||||
Displacement is on the cusp and the other variables don't
|
||||
have a terribly useful relationship with this median.
|
||||
|
||||
The boxplots indicate that acceleration really isn't a great
|
||||
predictor of mpg01, but displacement is. It also confirms
|
||||
horsepower and weight as good predictors, and cylinders also
|
||||
seems to be very strong, even though I didn't take that from
|
||||
the scatter plots.
|
||||
|
||||
I will use mpg01 ~ horsepower + weight + cylinders + displacement
|
||||
|
||||
|
||||
──────────────────────────────────────────────────────────────────────────
|
||||
(c) Split the data into a training set and a test set.
|
||||
|
||||
Seems like a 50/50 random sampling is appropriate enough.
|
||||
@ -127,87 +142,133 @@ Part B: Choose one of Questions 10 or 11
|
||||
> train_bools = rep(F,length(auto$mpg))
|
||||
> train_bools[training_indices]=T
|
||||
> head(train_bools)
|
||||
[1] FALSE TRUE FALSE FALSE TRUE FALSE
|
||||
[1] TRUE TRUE TRUE FALSE TRUE FALSE
|
||||
> length(train_bools)
|
||||
[1] 397
|
||||
> train_data = auto[train_bools,]
|
||||
> test_data = auto[!train_bools,]
|
||||
|
||||
Actually, I changed this now, because a solution I found
|
||||
online suggested a different test split and I was having
|
||||
trouble with the KNN model, so I followed their style. I used:
|
||||
|
||||
> train <- (auto$year %% 2 == 0)
|
||||
|
||||
and then the rest the same
|
||||
|
||||
──────────────────────────────────────────────────────────────────────────
|
||||
(d) Perform LDA on the training data in order to predict mpg01
|
||||
using the variables that seemed most associated with mpg01 in
|
||||
(b). What is the test error of the model obtained?
|
||||
|
||||
> lda.fit
|
||||
Call:
|
||||
lda(mpg01 ~ horsepower + weight + acceleration + displacement,
|
||||
data = train_data)
|
||||
lda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data)
|
||||
|
||||
Prior probabilities of groups:
|
||||
0 1
|
||||
0.5431472 0.4568528
|
||||
0.4666667 0.5333333
|
||||
|
||||
Group means:
|
||||
horsepower weight acceleration displacement
|
||||
0 129.08411 3557.757 14.55981 269.729
|
||||
1 79.64444 2345.233 16.39222 116.800
|
||||
horsepower weight cylinders displacement
|
||||
0 131.96939 3579.827 6.755102 268.4082
|
||||
1 77.96429 2313.598 4.071429 111.7188
|
||||
|
||||
Coefficients of linear discriminants:
|
||||
LD1
|
||||
horsepower 0.005678626
|
||||
weight -0.001137499
|
||||
acceleration -0.014950459
|
||||
displacement -0.007401647
|
||||
LD1
|
||||
horsepower 0.0060634365
|
||||
weight -0.0011442212
|
||||
cylinders -0.6390942259
|
||||
displacement 0.0004517291
|
||||
|
||||
|
||||
Error Rate against test data:
|
||||
|
||||
***Test Data Error Rate:
|
||||
> mean(lda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
[1] 0.1179487
|
||||
[1] 0.1428571
|
||||
|
||||
|
||||
|
||||
──────────────────────────────────────────────────────────────────────────
|
||||
(e) Perform QDA on the training data in order to predict mpg01
|
||||
using the variables that seemed most associated with mpg01 in
|
||||
(b). What is the test error of the model obtained?
|
||||
|
||||
> qda.fit=qda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
|
||||
> qda.fit
|
||||
Call:
|
||||
qda(mpg01 ~ horsepower + weight + acceleration + displacement,
|
||||
data = train_data)
|
||||
lda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data)
|
||||
|
||||
Prior probabilities of groups:
|
||||
0 1
|
||||
0.5431472 0.4568528
|
||||
0.4666667 0.5333333
|
||||
|
||||
Group means:
|
||||
horsepower weight acceleration displacement
|
||||
0 129.08411 3557.757 14.55981 269.729
|
||||
1 79.64444 2345.233 16.39222 116.800
|
||||
horsepower weight cylinders displacement
|
||||
0 131.96939 3579.827 6.755102 268.4082
|
||||
1 77.96429 2313.598 4.071429 111.7188
|
||||
|
||||
Error Rate:
|
||||
Coefficients of linear discriminants:
|
||||
LD1
|
||||
horsepower 0.0060634365
|
||||
weight -0.0011442212
|
||||
cylinders -0.6390942259
|
||||
displacement 0.0004517291
|
||||
|
||||
***Test Data Error Rate:
|
||||
> mean(qda.pred$class!=test_data$mpg01,na.rm=T)
|
||||
[1] 0.1025641
|
||||
[1] 0.1428571
|
||||
|
||||
|
||||
|
||||
──────────────────────────────────────────────────────────────────────────
|
||||
(f) Perform logistic regression on the training data in order to
|
||||
pre- dict mpg01 using the variables that seemed most associated
|
||||
with mpg01 in (b). What is the test error of the model obtained?
|
||||
|
||||
> glm.fit=glm(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data,family=binomial)
|
||||
> glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial)
|
||||
> glm.probs=predict(glm.fit,test_data,type="response")
|
||||
> glm.pred=rep(0,199)
|
||||
> glm.pred[glm.probs>.5]=1
|
||||
|
||||
***Test Data Error Rate:
|
||||
> mean(glm.pred!=test_data$mpg01)
|
||||
[1] 0.120603
|
||||
[1] 0.1407035
|
||||
|
||||
|
||||
──────────────────────────────────────────────────────────────────────────
|
||||
(g) Perform KNN on the training data, with several values of K,
|
||||
in order to predict mpg01 . Use only the variables that seemed
|
||||
most associated with mpg01 in (b). What test errors do you
|
||||
obtain? Which value of K seems to perform the best on this data
|
||||
set?
|
||||
|
||||
The knn method can't handle the NA values, so
|
||||
|
||||
> set.seed(1)
|
||||
> auto <- na.omit(auto)
|
||||
> train_bools <- (auto$year %% 2 == 0)
|
||||
> train_data = auto[train_bools,]
|
||||
> test_data = auto[!train_bools,]
|
||||
|
||||
> train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
|
||||
> test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
|
||||
> train.mpg01 = auto$mpg01[train_bools]
|
||||
|
||||
***Test Data Error Rates:
|
||||
k = 1
|
||||
> mean(knn.pred != test_data$mpg01)
|
||||
[1] 0.1483516
|
||||
k = 2
|
||||
> mean(knn.pred != test_data$mpg01)
|
||||
[1] 0.1593407
|
||||
k = 3
|
||||
> mean(knn.pred != test_data$mpg01)
|
||||
[1] 0.1648352
|
||||
k = 4
|
||||
> mean(knn.pred != test_data$mpg0)
|
||||
[1] 0.1813187
|
||||
|
||||
k = 1 looks like the best, since the error rate increases with k.
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user