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45 lines
1.3 KiB
Plaintext
45 lines
1.3 KiB
Plaintext
library(ISLR)
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library(MASS)
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library(class)
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auto = read.table("auto.data",header=T,na.strings="?")
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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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sample(auto,size=length(mpg01)/2)
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train_bools <- (auto$year %% 2 == 0)
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train_data = auto[train_bools,]
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test_data = auto[!train_bools,]
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lda.fit=lda(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data)
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lda.pred=predict(lda.fit, test_data)
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mean(lda.pred$class!=test_data$mpg01,na.rm=T)
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qda.fit=qda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
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qda.pred=predict(qda.fit,test_data,na.rm=T)
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mean(qda.pred$class!=test_data$mpg01,na.rm=T)
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glm.fit=glm(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data,family=binomial)
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glm.probs=predict(glm.fit,test_data,type="response")
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glm.pred=rep(0,199)
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glm.pred[glm.probs>.5]=1
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mean(glm.pred!=test_data$mpg01)
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set.seed(1)
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auto <- na.omit(auto)
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train_bools <- (auto$year %% 2 == 0)
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train_data = auto[train_bools,]
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test_data = auto[!train_bools,]
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train.X = cbind(auto$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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knn.pred = knn(train.X,test.X,train.mpg01,k=1)
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