mirror of
https://asciireactor.com/otho/cs-5821.git
synced 2024-11-22 02:25:06 +00:00
250 lines
8.8 KiB
R
250 lines
8.8 KiB
R
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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library(ISLR)
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library(MASS)
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library(class)
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train_bools <- (auto$year %% 2 == 0)
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train_data = auto[train_bools,]
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test_data = auto[!train_bools,]
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help(knn)
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help(knn)
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train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
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test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
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train
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test
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?knn
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cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
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cl
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length(cl)
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length(train)
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nrows(train)
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nrow(train)
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train.X
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train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
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train.X
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test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
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test.X
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train.X
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train.mpg01 = auto$mpg01[train_bools]
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train.mpg01
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length(train.mpg01)
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nrow(train.X)
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knn(train.X,train.Y,train.mpg01,K=1)
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knn(train.X,train.Y,train.mpg01,k=1)
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knn(train.X,test.X,train.mpg01,k=1)
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train.X
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na.omit(train.X)
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?na.omit
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na.omit(train.X)
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na.omit(train.X)
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knn(na.omit(train.X),test.X,train.mpg01,k=1)
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knn(na.omit(train.X),test.X,na.omit(train.mpg01),k=1)
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knn(na.omit(train.X),na.omit(test.X),na.omit(train.mpg01),k=1)
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train.mpg012 = na.omit(auto$mpg01)[train_bools]
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train.mpg012
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train.mpg01
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nrow(train)
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na.omit(auto)
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auto
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na.omit(auto)
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summary(auto)
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summary(na.omit(auto))
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Auto = na.omit(auto)
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auto = na.omit(auto)
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ncol(auto)
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nrow(auto)
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auto <- na.omit(auto)
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train_bools <- (auto$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$mpg01[train_bools]
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knn.pred = knn(train.X,test.X,train.mpg01,k=1)
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mean(knn.pred != auto$mpg01)
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mean(knn.pred != test_data$mpg01)
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knn.pred = knn(train.X,test.X,train.mpg01,k=2)
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mean(knn.pred != test_data$mpg01)
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knn.pred = knn(train.X,test.X,train.mpg01,k=3)
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mean(knn.pred != test_data$mpg01)
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knn.pred = knn(train.X,test.X,train.mpg01,k=4)
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mean(knn.pred != test_data$mpg0)
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knn.pred
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length(knn.pred)
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dim(knn.pred)
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length(test_data)
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ncol(test_data)
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nrow(test_data)
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q()
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qda.fit
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fit.qda
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qda.fit
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auto
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qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data)
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import(MASS)
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qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data)
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import(class)
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library(MASS)
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qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data)
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qda.fit
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> mean(qda.pred$class!=test_data$mpg01,na.rm=T)
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mean(qda.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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qda.fit=qda(mpg01 ~ horsepower + weight + cylinders + 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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qda.fit
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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 + cylinders + 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$mpg01)
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glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial)
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glm.probs=predict(glm.fit,test_data,type="response")
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glm.pred=rep(0,length(test_data)
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glm.pred[glm.probs>.5]=1
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mean(glm.pred!=test_data$mpg01)
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glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + 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,length(test_data))
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glm.pred[glm.probs>.5]=1
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mean(glm.pred!=test_data$mpg01)
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glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial)
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glm.probs=predict(glm.fit,test_data,type="response")
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glm.pred=rep(0,length(test_data))
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glm.pred[glm.probs>.5]=1
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mean(glm.pred!=test_data$mpg01)
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glm.pred
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glm.pred=rep(0,length(test_data))
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glm.pred
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test_data
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glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + 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,nrow(test_data))
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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$weight,auto$cylinders)[train_bools,]
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test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$cylinders)[!train_bools,]
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train.mpg01 = auto$mpg01[train_bools]
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knn.pred = knn(train.X,test.X,train.mpg01,k=1)
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mean(knn.pred != test_data$mpg01)
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knn.pred = knn(train.X,test.X,train.mpg01,k=2)
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mean(knn.pred != test_data$mpg01)
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knn.pred = knn(train.X,test.X,train.mpg01,k=3)
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mean(knn.pred != test_data$mpg01)
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knn.pred = knn(train.X,test.X,train.mpg01,k=4)
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mean(knn.pred != test_data$mpg0)
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import(class)
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library(class)
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knn.pred = knn(train.X,test.X,train.mpg01,k=1)
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mean(knn.pred != test_data$mpg01)
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knn.pred = knn(train.X,test.X,train.mpg01,k=2)
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mean(knn.pred != test_data$mpg01)
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knn.pred = knn(train.X,test.X,train.mpg01,k=3)
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mean(knn.pred != test_data$mpg01)
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knn.pred = knn(train.X,test.X,train.mpg01,k=4)
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mean(knn.pred != test_data$mpg0)
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q()
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library(boot)
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library(MASS)
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library(ISLr)
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library(ISLR)
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data(Default)
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set.seed(45)
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fit.glm = glm(default ~ income + balance,Default)
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fit.glm = glm(default ~ income + balance,Default,family="binomial)
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fit.glm = glm(default ~ income + balance,Default,family="binomial")
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summary(glm)
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summary(fit.glm)
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fit.glm = glm(default ~ income + balance,Default)
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fit.glm = glm(default ~ income + balance,data = Default)
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fit.glm = glm(default ~ income + balance,Default,family="binomial")
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length(Default)
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length(Default$student)
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train.default1 = default[1:6001,]
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train.default1 = Default[1:6001,]
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train.default2 = Default[1:5001,]
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test.default1 = Default[!1:6001,]
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test.default1
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head(test.default1)
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test.default1$student
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test.default1 = Default[6002:10000,]
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head(test.default1)
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test.default2 = Default[5002:10000,]
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fit.glm.default1 = glm.fit(default ~ income + balance,data=Default,family="binomial")
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fit.glm.default1 = glm.fit(default ~ income + balance,Default,family="binomial")
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fit.glm = glm(default ~ income + balance,Default,family="binomial")
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fit.glm.default1 = glm(default ~ income + balance,Default,family="binomial")
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fit.glm.default1 = glm(default ~ income + balance,train.default1,family="binomial")
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summary(fit.glm.default1)
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fit.glm.default2 = glm(default ~ income + balance,train.default2,family="binomial")
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fit.glm.default1 = glm(default ~ income + balance,train.default1,family="binomial")
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fit.glm.default2 = glm(default ~ income + balance,train.default2,family="binomial")
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fit.glm.default1.prob = predict(fit.glm.default1,test.default1,type="response")
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fit.glm.default1.prob
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fit.glm.default1.pred = rep("No",nrow(test.default1))
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fit.glm.default1.pred
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fit.glm.default1.pred[fit.glm.default1.prob>0.5] = "Yes"
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fit.glm.default1.pred
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fit.glm.default1.pred[fit.glm.default1.prob>0.5] = "Yes"fit.glm.default1.pred = rep("No",nrow(test.default1))
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table(fit.glm.default1.pred,test.default1)
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fit.glm.default1.pred[fit.glm.default1.prob>0.5] = "Yes"
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fit.glm.default1.pred
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test.default1
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length(test.default1$student)
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length(fit.glm.default1.pred)
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table(fit.glm.default1.pred,test.default1$default)
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1 - (3851+44)/(3851+90+14+44)
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fit.glm.default2.prob = predict(fit.glm.default2,test.default2,type="response")
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fit.glm.default2.pred = rep("No",nrow(test.default2))
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fit.glm.default2.pred[fit.glm.default2.prob > 0.5] = "Yes"
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table(fit.glm.default.pred,test.default2$default)
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table(fit.glm.default2.pred,test.default2$default)
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1 - (4818+52)/(4818+106+23+52)
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summary(fit.glm.default2)
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summary(fit.glm.default1)
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summary(fit.glm.default2)
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coefficients(fit.glm.default1)
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fit.glm.default1$coefficients
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fit.glm.default1$coefficients[1,2]
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fit.glm.default1$coefficients[1:2]
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fit.glm.default1$coefficients[2:3]
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boot.fn = function(Default,index){
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model = glm(default ~ income + balance,Default,family="binomial",subset=index)
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fit.glm.default1$coefficients[2:3]
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}
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boot.fn(Default,c(14,5,79))
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boot.fn(Default,c(14,5,79,324,6435,234))
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boot.fn(Default,seq(15:7000))
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boot.fn = function(Default,index){
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model = glm(default ~ income + balance,Default,family="binomial",subset=index)
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model$coefficients[2:3]
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}
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boot.fn = function(Default,index){
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boot.fn = function(Default,index){
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model = glm(default ~ income + balance,Default,family="binomial",subset=index)
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model$coefficients[2:3]
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}
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boot.fn(Default,seq(15:7000))
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boot.fn(Default,seq(15:3000))
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boot.fn(Default,seq(15:3050))
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boot.fn(Default,seq(15:3500))
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set.seed(56)
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?boot
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boot(Default,boot.fn,c(1:1000))
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?boot
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boot(Default,boot.fn,1000)
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4.68/7.06
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2.32/3.232
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q()
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