From 970e6e36d59552c09c3e0b63a36ff77a7353681a Mon Sep 17 00:00:00 2001 From: caes Date: Thu, 9 Feb 2017 23:12:09 -0500 Subject: [PATCH] fixed errors --- hw3/.RData | Bin 27709 -> 66455 bytes hw3/.Rhistory | 78 ++++++++++++++++++++++++++++++++++++++++++++++++++ hw3/answers | 31 ++++++++++---------- hw3/commands | 20 +++++++++---- 4 files changed, 109 insertions(+), 20 deletions(-) diff --git a/hw3/.RData b/hw3/.RData index 75b0d6ac764417f1f1e8b836c49c6942207a6545..97b22f9a64f582697a747df6999701b42c9fb3c1 100644 GIT binary patch literal 66455 zcmeF%XE>bS{wRFCGlb}j5=8Wp41yV5^b#cr88rw|CZaPs(L0GYqK7C^qW7r5=%Uv| z4aVp*9KZcLXYc<$*LnVLp7Z9po;|PDwbpl)`@Yu9eXsd`fYBs4|M=kVwGw*KJvo+= z%?w@Pq&8z9?^HCadbzMDmubUYMPo%R+0g8(c*lw$_;F3c)oMV5BNCtLqR8{}D^Hnc zD}P$g{$Ov~HoST3%!PQ&rxqn9rY0I4bc_Auj@?-8dOwh-e`q|M=+ZhbUJ-0)k<5#W zAz77r%d!d*vg5JQHkNj~1gFYDv>xNQ+n^jj1fKE^RyzO!ohjr@0#9(J3AY9&k8M!Z zFYgk~;g$E)zQ}dx`x)92sdlY35R0U;pd_;LPf33>lf7ETiBH<0Dh89*3osU$%^`WoKNDXvu{-Yxx_+ko-J~ 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z7+1CwTB2Jq?+Fy+y-%x4F>ljI8V0sH@<%UK-Z#rwPj!26>+!n_|2+3H^2aj|&rj40 z99*b*Un9Pct>5^(c#CmME@=F^1k`B!I;o~`Ti2uQ9q{oaBLh!=D keSq;pPQ$?KFV#WxbcKAA{g+DTtwqYcTIb!Fid#1QA2;YnH2?qr diff --git a/hw3/.Rhistory b/hw3/.Rhistory index 3a641a7..ee5aad7 100644 --- a/hw3/.Rhistory +++ b/hw3/.Rhistory @@ -77,3 +77,81 @@ length(test_data) ncol(test_data) nrow(test_data) q() +qda.fit +fit.qda +qda.fit +auto +qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data) +import(MASS) +qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data) +import(class) +library(MASS) +qda.fit = qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data) +qda.fit +> mean(qda.pred$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() diff --git a/hw3/answers b/hw3/answers index a4e2011..56b2827 100644 --- a/hw3/answers +++ b/hw3/answers @@ -196,7 +196,7 @@ Part B: Choose one of Questions 10 or 11 > qda.fit Call: - lda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data) + qda(mpg01 ~ horsepower + weight + cylinders + displacement, data = train_data) Prior probabilities of groups: 0 1 @@ -206,17 +206,12 @@ Part B: Choose one of Questions 10 or 11 horsepower weight cylinders displacement 0 131.96939 3579.827 6.755102 268.4082 1 77.96429 2313.598 4.071429 111.7188 - - Coefficients of linear discriminants: - LD1 - horsepower 0.0060634365 - weight -0.0011442212 - cylinders -0.6390942259 - displacement 0.0004517291 + + > qda.pred=predict(qda.fit,test_data,na.rm=T) ***Test Data Error Rate: > mean(qda.pred$class!=test_data$mpg01,na.rm=T) - [1] 0.1428571 + [1] 0.1483516 @@ -227,12 +222,12 @@ Part B: Choose one of Questions 10 or 11 > 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=rep(0,nrow(test_data)) > glm.pred[glm.probs>.5]=1 ***Test Data Error Rate: > mean(glm.pred!=test_data$mpg01) - [1] 0.1407035 + [1] 0.1373626 ────────────────────────────────────────────────────────────────────────── @@ -249,24 +244,30 @@ Part B: Choose one of Questions 10 or 11 > train_bools <- (auto$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.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$mpg01[train_bools] + ***Test Data Error Rates: k = 1 + > knn.pred = knn(train.X,test.X,train.mpg01,k=1) > mean(knn.pred != test_data$mpg01) [1] 0.1483516 k = 2 + > knn.pred = knn(train.X,test.X,train.mpg01,k=2) > mean(knn.pred != test_data$mpg01) [1] 0.1593407 k = 3 + > knn.pred = knn(train.X,test.X,train.mpg01,k=3) > mean(knn.pred != test_data$mpg01) [1] 0.1648352 k = 4 + > knn.pred = knn(train.X,test.X,train.mpg01,k=4) > mean(knn.pred != test_data$mpg0) - [1] 0.1813187 + [1] 0.1923077 + k = 1 looks like the best, since the error rate increases with k. diff --git a/hw3/commands b/hw3/commands index 6991c04..9505819 100644 --- a/hw3/commands +++ b/hw3/commands @@ -22,13 +22,13 @@ lda.fit=lda(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_da lda.pred=predict(lda.fit, test_data) mean(lda.pred$class!=test_data$mpg01,na.rm=T) -qda.fit=qda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data) +qda.fit=qda(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data) qda.pred=predict(qda.fit,test_data,na.rm=T) mean(qda.pred$class!=test_data$mpg01,na.rm=T) -glm.fit=glm(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data,family=binomial) +glm.fit=glm(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data,family=binomial) glm.probs=predict(glm.fit,test_data,type="response") -glm.pred=rep(0,199) +glm.pred=rep(0,nrow(test_data)) glm.pred[glm.probs>.5]=1 mean(glm.pred!=test_data$mpg01) @@ -39,8 +39,18 @@ 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.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)