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$mpg0)
q()
library(boot)
library(MASS)
library(ISLr)
library(ISLR)
data(Default)
set.seed(45)
fit.glm = glm(default ~ income + balance,Default)
fit.glm = glm(default ~ income + balance,Default,family="binomial)
fit.glm = glm(default ~ income + balance,Default,family="binomial")
summary(glm)
summary(fit.glm)
fit.glm = glm(default ~ income + balance,Default)
fit.glm = glm(default ~ income + balance,data = Default)
fit.glm = glm(default ~ income + balance,Default,family="binomial")
length(Default)
length(Default$student)
train.default1 = default[1:6001,]
train.default1 = Default[1:6001,]
train.default2 = Default[1:5001,]
test.default1 = Default[!1:6001,]
test.default1
head(test.default1)
test.default1$student
test.default1 = Default[6002:10000,]
head(test.default1)
test.default2 = Default[5002:10000,]
fit.glm.default1 = glm.fit(default ~ income + balance,data=Default,family="binomial")
fit.glm.default1 = glm.fit(default ~ income + balance,Default,family="binomial")
fit.glm = glm(default ~ income + balance,Default,family="binomial")
fit.glm.default1 = glm(default ~ income + balance,Default,family="binomial")
fit.glm.default1 = glm(default ~ income + balance,train.default1,family="binomial")
summary(fit.glm.default1)
fit.glm.default2 = glm(default ~ income + balance,train.default2,family="binomial")
fit.glm.default1 = glm(default ~ income + balance,train.default1,family="binomial")
fit.glm.default2 = glm(default ~ income + balance,train.default2,family="binomial")
fit.glm.default1.prob = predict(fit.glm.default1,test.default1,type="response")
fit.glm.default1.prob
fit.glm.default1.pred = rep("No",nrow(test.default1))
fit.glm.default1.pred
fit.glm.default1.pred[fit.glm.default1.prob>0.5] = "Yes"
fit.glm.default1.pred
fit.glm.default1.pred[fit.glm.default1.prob>0.5] = "Yes"fit.glm.default1.pred = rep("No",nrow(test.default1))
table(fit.glm.default1.pred,test.default1)
fit.glm.default1.pred[fit.glm.default1.prob>0.5] = "Yes"
fit.glm.default1.pred
test.default1
length(test.default1$student)
length(fit.glm.default1.pred)
table(fit.glm.default1.pred,test.default1$default)
1 - (3851+44)/(3851+90+14+44)
fit.glm.default2.prob = predict(fit.glm.default2,test.default2,type="response")
fit.glm.default2.pred = rep("No",nrow(test.default2))
fit.glm.default2.pred[fit.glm.default2.prob > 0.5] = "Yes"
table(fit.glm.default.pred,test.default2$default)
table(fit.glm.default2.pred,test.default2$default)
1 - (4818+52)/(4818+106+23+52)
summary(fit.glm.default2)
summary(fit.glm.default1)
summary(fit.glm.default2)
coefficients(fit.glm.default1)
fit.glm.default1$coefficients
fit.glm.default1$coefficients[1,2]
fit.glm.default1$coefficients[1:2]
fit.glm.default1$coefficients[2:3]
        boot.fn = function(Default,index){
            model = glm(default ~ income + balance,Default,family="binomial",subset=index)
            fit.glm.default1$coefficients[2:3]
        }
boot.fn(Default,c(14,5,79))
boot.fn(Default,c(14,5,79,324,6435,234))
boot.fn(Default,seq(15:7000))
        boot.fn = function(Default,index){
            model = glm(default ~ income + balance,Default,family="binomial",subset=index)
            model$coefficients[2:3]
        }
        boot.fn = function(Default,index){
        boot.fn = function(Default,index){
            model = glm(default ~ income + balance,Default,family="binomial",subset=index)
            model$coefficients[2:3]
        }
boot.fn(Default,seq(15:7000))
boot.fn(Default,seq(15:3000))
boot.fn(Default,seq(15:3050))
boot.fn(Default,seq(15:3500))
set.seed(56)
?boot
boot(Default,boot.fn,c(1:1000))
?boot
boot(Default,boot.fn,1000)
4.68/7.06
2.32/3.232
q()