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463 lines
12 KiB
Plaintext
463 lines
12 KiB
Plaintext
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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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$mpg01)
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library(class)
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?cbind
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?knn
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knn.fit = knn(train_data,test_data,auto$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)
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length(train.mpg01)
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length(test.X)
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text.X
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test.X
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test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
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length(test.X)
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test.X
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knn.pred = knn(train.X,test.X,train.mpg01,k=1)
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train.X
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train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
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train.X
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test.X
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knn.pred = knn(train.X,test.X,train.mpg01,k=1)
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?knn
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length(train.X)
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length(train.X[1,])
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length(train.X[,1])
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?knn
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plot(auto)
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train.X = cbind(auto$horsepower,auto$displacement)[train_bools,]
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test.X = cbind(auto$horsepower,auto$displacement)[!train_bools,]
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train.mpg01 = auto$mpg01[train_bools]
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knn.pred = knn(train.X,test.X,train.mpg01,k=1)
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train.X
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test.X
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train.mpg01
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knn.pred = knn(train.X,test.X,train.mpg01,k=1)
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q()
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train
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train.X
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train.X
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test.X
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p1 = seq(1:1))
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p1 = 1:10
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p1
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p1 = ,1:10
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p2 = 10:20
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p2
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cbind(p1,p2)
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p3=c(1,2,3,4,5,7,9,8,10)
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p4=c(10,11,12,13,14,15,16,17,18,29,20)
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|
px = cbind(p1,p2)
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py = cbind(p3,p4)
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|
py
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|
?formula
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|
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
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|
train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
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|
train.mpg01 = auto$mpg01[train_bools]
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|
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
|
import(library)
|
|
library(MASS)
|
|
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
|
library(library)
|
|
library(class)
|
|
knn.pred = knn(train.X,test.X,train.mpg01,k=1)
|
|
knn.pred
|
|
summary(test_data)
|
|
summary(train_data)
|
|
fix(train_data)
|
|
?fix
|
|
fix(test_data)
|
|
test_data.fix
|
|
test_data.fix()
|
|
fix(test_data)
|
|
q()
|