diff --git a/hw3/.RData b/hw3/.RData
deleted file mode 100644
index 97b22f9..0000000
Binary files a/hw3/.RData and /dev/null differ
diff --git a/hw3/.Rhistory b/hw3/.Rhistory
deleted file mode 100644
index ee5aad7..0000000
--- a/hw3/.Rhistory
+++ /dev/null
@@ -1,157 +0,0 @@
-auto = read.table("auto.data",header=T,na.strings="?")
-auto$mpg01=rep(0,397)
-auto$mpg01[auto$mpg>median(auto$mpg)]=1
-library(ISLR)
-library(MASS)
-library(class)
-train_bools <- (auto$year %% 2 == 0)
-train_data = auto[train_bools,]
-test_data = auto[!train_bools,]
-help(knn)
-help(knn)
-     train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
-     test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
-train
-test
-?knn
-     cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
-cl
-length(cl)
-length(train)
-nrows(train)
-nrow(train)
-train.X
-train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
-train.X
-test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
-test.X
-train.X
-train.mpg01 = auto$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()
-qda.fit
-fit.qda
-qda.fit
-auto
-qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data)
-import(MASS)
-qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data)
-import(class)
-library(MASS)
-qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data)
-qda.fit
-> mean(qda.pred$class!=test_data$mpg01,na.rm=T)
-mean(qda.pred$class!=test_data$mpg01,na.rm=T)
-qda.fit=qda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
-qda.pred=predict(qda.fit,test_data,na.rm=T)
-mean(qda.pred$class!=test_data$mpg01,na.rm=T)
-qda.fit=qda(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data)
-qda.pred=predict(qda.fit,test_data,na.rm=T)
-mean(qda.pred$class!=test_data$mpg01,na.rm=T)
-qda.fit
-qda.pred=predict(qda.fit,test_data,na.rm=T)
-mean(qda.pred$class!=test_data$mpg01,na.rm=T)
-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
-mean(glm.pred!=test_data$mpg01)
-glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial)
-glm.probs=predict(glm.fit,test_data,type="response")
-glm.pred=rep(0,length(test_data)
-glm.pred[glm.probs>.5]=1
-mean(glm.pred!=test_data$mpg01)
-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,length(test_data))
-glm.pred[glm.probs>.5]=1
-mean(glm.pred!=test_data$mpg01)
-glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial)
-glm.probs=predict(glm.fit,test_data,type="response")
-glm.pred=rep(0,length(test_data))
-glm.pred[glm.probs>.5]=1
-mean(glm.pred!=test_data$mpg01)
-glm.pred
-glm.pred=rep(0,length(test_data))
-glm.pred
-test_data
-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,nrow(test_data))
-glm.pred[glm.probs>.5]=1
-mean(glm.pred!=test_data$mpg01)
-set.seed(1)
-auto <- na.omit(auto)
-train_bools <- (auto$year %% 2 == 0)
-train_data = auto[train_bools,]
-test_data = auto[!train_bools,]
-train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$cylinders)[train_bools,]
-test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$cylinders)[!train_bools,]
-train.mpg01 = auto$mpg01[train_bools]
-knn.pred = knn(train.X,test.X,train.mpg01,k=1)
-mean(knn.pred != test_data$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$mpg0)
-import(class)
-library(class)
-knn.pred = knn(train.X,test.X,train.mpg01,k=1)
-mean(knn.pred != test_data$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)
-q()