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z`YstmhB;rDxl^#e0z{nMz1lehlDgdVD)a^ldfp=NVv7wMPB)i9ZLN0R;C}#6>)u)rv@Rbg=+`?_r#7wu#;4dq)egMn_ z$m@iiy20}~Ac+SEXwM9)Z|EKB{dg zI4*6v8zR%;==GVoWw;2jI3{fQ^s6g7e6!{DmDMmJOr5y$!*wr?0fXJg= zWVuw&d;hVl`#@TIQ#)ny0EZ`V{mYHunWWChd=R01_Ku<)e{?5xt=e}fFZnK$!uY$B zxnWVN9VKZ`M$D4}f4vWj*^jc$D<`hrW&4z-2DeVMWOc^S6ecQvN9*#fk0CuxOGY;l+S$Cp9VC{{?TKcZ8U4kUs8 z;`@3-#O(E8@aclxsCs@T@SSDLpWEu@aJaD__M$xX zPN#+GuK480=jOJo{J6^m9yE9wNV9>iM!Z=YxT$V-S9xRkBFOXc)_z%u8jEPJJCADo z(1{ESaQUGxF2F%od6*hT*zZ{p!<^2hJ1OrVunkguZ!xtMm(i(D!AZKdeM--9W#Y~D%w=cVLkP*Gn+Y`dqRV+BwfNwMq_wm=PM>Q_}zVJ zV70{WndsO2h{{zKmh4a;wLH5;WzxiFK+*I94tE>EeodEl=JdVSb#e}|Dq3v2=~50n z5^Am)C6dErrSs2akFRQ()1Sf>eijE$z8v`=)f7emB|6lg+A;oFtvV;5TI4FNHm8Iq z>!CVKP|Cd~2N6!lFLFK6T!T>Uq-!`Ns*Ub2K4ClVvI%~7>C3PXn-Qd&G&C7JN&V{n z!wmIK$ssF#@7MJp(Rc;Tb`AMy0}=Ks;K=fPbN-M|Uq6wXTa;U`MXe}Rd$P+qQH@Cy zQc&bSm6enMEmK!73ZK$U$Vk_%Q*bCN!=wzc>rZ9XRlH+T*CNvY$`+bHsjWajT5zNP zKaMHAr24L?|9Nyo)&K920ZME%Oz<`|R(<*sZ@#(Ai7g&p?H(_$#r;O@sip!QsiNvr z^`C-N;YsqBkx{X#6FPbYx%VZM*9q`%&u?BTU4D)_dHiIT^Q6_|{>t;;wAPT#lI*Hi zyC(w5n)dvG_uL)QvYSFW@K7I#+$5C#+jVYO^XoQ{Yy*v+x2j_woV4x#W+$+d;brn4 zLM%svTX^7XMvCEOa0BnTZ}-CK`R#+5s=FqZ+uQAiinRLF(`1=6nvbTgj9gowW(vJv z!r34^IrDVjei)SJ|Mb;s)}&sM-uCRvlPz(`cj7gXQt`QF;<(!V!~Q3LT^aBs{P;Mz z=7+PwKX)NnOI?hLEUlRa`BAWADZ7y?e?vj>y;bN*IIFASnMV7ds*vcSt0gtVoc=}! z;_?Z3{AmS5Dr5=Ac9L{D!7#!90_)5`!PRA>?U)YLe_f`xhGC#YQ1#`F{!P16puWcu zV)}d#v%Vg^75G~AI8hvgJ?3?>7()}f-%exh4DYKmC`6aP+~nQjq0$WslnR1b@WeJY acpl}GF)UNRG94_w1LE3ldHaswmedian(auto$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()