ANLY-530- Lab 1
Navneet Dhull
June 28, 2018
R Markdown
Download credit.csv from Moodle Prepare the dataset Implement the decision tree, Support vector machine and Recursive partitioning Evaluate the result
library(C50)
library(gmodels)
credit <-read.csv("~/credit.csv")
str(credit)## 'data.frame': 1000 obs. of 17 variables:
## $ checking_balance : Factor w/ 4 levels "< 0 DM","> 200 DM",..: 1 3 4 1 1 4 4 3 4 3 ...
## $ months_loan_duration: int 6 48 12 42 24 36 24 36 12 30 ...
## $ credit_history : Factor w/ 5 levels "critical","good",..: 1 2 1 2 4 2 2 2 2 1 ...
## $ purpose : Factor w/ 6 levels "business","car",..: 5 5 4 5 2 4 5 2 5 2 ...
## $ amount : int 1169 5951 2096 7882 4870 9055 2835 6948 3059 5234 ...
## $ savings_balance : Factor w/ 5 levels "< 100 DM","> 1000 DM",..: 5 1 1 1 1 5 4 1 2 1 ...
## $ employment_duration : Factor w/ 5 levels "< 1 year","> 7 years",..: 2 3 4 4 3 3 2 3 4 5 ...
## $ percent_of_income : int 4 2 2 2 3 2 3 2 2 4 ...
## $ years_at_residence : int 4 2 3 4 4 4 4 2 4 2 ...
## $ age : int 67 22 49 45 53 35 53 35 61 28 ...
## $ other_credit : Factor w/ 3 levels "bank","none",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ housing : Factor w/ 3 levels "other","own",..: 2 2 2 1 1 1 2 3 2 2 ...
## $ existing_loans_count: int 2 1 1 1 2 1 1 1 1 2 ...
## $ job : Factor w/ 4 levels "management","skilled",..: 2 2 4 2 2 4 2 1 4 1 ...
## $ dependents : int 1 1 2 2 2 2 1 1 1 1 ...
## $ phone : Factor w/ 2 levels "no","yes": 2 1 1 1 1 2 1 2 1 1 ...
## $ default : Factor w/ 2 levels "no","yes": 1 2 1 1 2 1 1 1 1 2 ...summary(credit$amount)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 250 1366 2320 3271 3972 18424table(credit$default)##
## no yes
## 700 300set.seed(12345)
credit_rand <-credit[order(runif(1000)),]
summary(credit_rand$default)## no yes
## 700 300credit_train <-credit_rand[1:900, ]
credit_test <-credit_rand[901:1000, ]
prop.table(table(credit_train$default))##
## no yes
## 0.7022222 0.2977778prop.table(table(credit_test$default))##
## no yes
## 0.68 0.32credit_model <-C5.0(x=credit_train[-17], y=credit_train$default)
summary(credit_model)##
## Call:
## C5.0.default(x = credit_train[-17], y = credit_train$default)
##
##
## C5.0 [Release 2.07 GPL Edition] Sat Jun 30 16:37:54 2018
## -------------------------------
##
## Class specified by attribute `outcome'
##
## Read 900 cases (17 attributes) from undefined.data
##
## Decision tree:
##
## checking_balance = unknown: no (358/44)
## checking_balance in {< 0 DM,> 200 DM,1 - 200 DM}:
## :...credit_history in {perfect,very good}:
## :...dependents > 1: yes (10/1)
## : dependents <= 1:
## : :...savings_balance = < 100 DM: yes (39/11)
## : savings_balance in {> 1000 DM,500 - 1000 DM,unknown}: no (8/1)
## : savings_balance = 100 - 500 DM:
## : :...checking_balance = < 0 DM: no (1)
## : checking_balance in {> 200 DM,1 - 200 DM}: yes (5/1)
## credit_history in {critical,good,poor}:
## :...months_loan_duration <= 11: no (87/14)
## months_loan_duration > 11:
## :...savings_balance = > 1000 DM: no (13)
## savings_balance in {< 100 DM,100 - 500 DM,500 - 1000 DM,unknown}:
## :...checking_balance = > 200 DM:
## :...dependents > 1: yes (3)
## : dependents <= 1:
## : :...credit_history in {good,poor}: no (23/3)
## : credit_history = critical:
## : :...amount <= 2337: yes (3)
## : amount > 2337: no (6)
## checking_balance = 1 - 200 DM:
## :...savings_balance = unknown: no (34/6)
## : savings_balance in {< 100 DM,100 - 500 DM,500 - 1000 DM}:
## : :...months_loan_duration > 45: yes (11/1)
## : months_loan_duration <= 45:
## : :...other_credit = store:
## : :...age <= 35: yes (4)
## : : age > 35: no (2)
## : other_credit = bank:
## : :...years_at_residence <= 1: no (3)
## : : years_at_residence > 1:
## : : :...existing_loans_count <= 1: yes (5)
## : : existing_loans_count > 1:
## : : :...percent_of_income <= 2: no (4/1)
## : : percent_of_income > 2: yes (3)
## : other_credit = none:
## : :...job = unemployed: no (1)
## : job = management:
## : :...amount <= 7511: no (10/3)
## : : amount > 7511: yes (7)
## : job = unskilled: [S1]
## : job = skilled:
## : :...dependents <= 1: no (55/15)
## : dependents > 1:
## : :...age <= 34: no (3)
## : age > 34: yes (4)
## checking_balance = < 0 DM:
## :...job = management: no (26/6)
## job = unemployed: yes (4/1)
## job = unskilled:
## :...employment_duration in {4 - 7 years,
## : : unemployed}: no (4)
## : employment_duration = < 1 year:
## : :...other_credit = bank: no (1)
## : : other_credit in {none,store}: yes (11/2)
## : employment_duration = > 7 years:
## : :...other_credit in {bank,none}: no (5/1)
## : : other_credit = store: yes (2)
## : employment_duration = 1 - 4 years:
## : :...age <= 39: no (14/3)
## : age > 39:
## : :...credit_history in {critical,good}: yes (3)
## : credit_history = poor: no (1)
## job = skilled:
## :...credit_history = poor:
## :...savings_balance in {< 100 DM,100 - 500 DM,
## : : 500 - 1000 DM}: yes (8)
## : savings_balance = unknown: no (1)
## credit_history = critical:
## :...other_credit = store: no (0)
## : other_credit = bank: yes (4)
## : other_credit = none:
## : :...savings_balance in {100 - 500 DM,
## : : unknown}: no (1)
## : savings_balance = 500 - 1000 DM: yes (1)
## : savings_balance = < 100 DM:
## : :...months_loan_duration <= 13:
## : :...percent_of_income <= 3: yes (3)
## : : percent_of_income > 3: no (3/1)
## : months_loan_duration > 13:
## : :...amount <= 5293: no (10/1)
## : amount > 5293: yes (2)
## credit_history = good:
## :...existing_loans_count > 1: yes (5)
## existing_loans_count <= 1:
## :...other_credit = store: no (2)
## other_credit = bank:
## :...percent_of_income <= 2: yes (2)
## : percent_of_income > 2: no (6/1)
## other_credit = none: [S2]
##
## SubTree [S1]
##
## employment_duration in {< 1 year,1 - 4 years}: yes (11/3)
## employment_duration in {> 7 years,4 - 7 years,unemployed}: no (8)
##
## SubTree [S2]
##
## savings_balance = 100 - 500 DM: yes (3)
## savings_balance = 500 - 1000 DM: no (1)
## savings_balance = unknown:
## :...phone = no: yes (9/1)
## : phone = yes: no (3/1)
## savings_balance = < 100 DM:
## :...percent_of_income <= 1: no (4)
## percent_of_income > 1:
## :...phone = yes: yes (10/1)
## phone = no:
## :...purpose in {business,car0,education,renovations}: yes (3)
## purpose = car:
## :...percent_of_income <= 3: no (2)
## : percent_of_income > 3: yes (6/1)
## purpose = furniture/appliances:
## :...years_at_residence <= 1: no (4)
## years_at_residence > 1:
## :...housing = other: no (1)
## housing = rent: yes (2)
## housing = own:
## :...amount <= 1778: no (3)
## amount > 1778:
## :...years_at_residence <= 3: yes (6)
## years_at_residence > 3: no (3/1)
##
##
## Evaluation on training data (900 cases):
##
## Decision Tree
## ----------------
## Size Errors
##
## 66 125(13.9%) <<
##
##
## (a) (b) <-classified as
## ---- ----
## 609 23 (a): class no
## 102 166 (b): class yes
##
##
## Attribute usage:
##
## 100.00% checking_balance
## 60.22% credit_history
## 53.22% months_loan_duration
## 49.44% savings_balance
## 30.89% job
## 25.89% other_credit
## 17.78% dependents
## 9.67% existing_loans_count
## 7.22% percent_of_income
## 6.67% employment_duration
## 5.78% phone
## 5.56% amount
## 3.78% years_at_residence
## 3.44% age
## 3.33% purpose
## 1.67% housing
##
##
## Time: 0.0 secscredit_pred <-predict(credit_model, credit_test)
CrossTable(credit_test$default, credit_pred, prop.chisq = FALSE, prop.c = FALSE, prop.r = FALSE, dnn=c('actual default', 'predicted default'))##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 100
##
##
## | predicted default
## actual default | no | yes | Row Total |
## ---------------|-----------|-----------|-----------|
## no | 57 | 11 | 68 |
## | 0.570 | 0.110 | |
## ---------------|-----------|-----------|-----------|
## yes | 16 | 16 | 32 |
## | 0.160 | 0.160 | |
## ---------------|-----------|-----------|-----------|
## Column Total | 73 | 27 | 100 |
## ---------------|-----------|-----------|-----------|
##
## (p <-table(credit_pred, credit_test$default))##
## credit_pred no yes
## no 57 16
## yes 11 16(Accuracy <- sum(diag(p))/sum(p)*100)## [1] 73Including Plots
You can also embed plots, for example:
library(kernlab)
letter <-read.csv("~/letterdata.csv")
str(letter)## 'data.frame': 20000 obs. of 17 variables:
## $ letter: Factor w/ 26 levels "A","B","C","D",..: 20 9 4 14 7 19 2 1 10 13 ...
## $ xbox : int 2 5 4 7 2 4 4 1 2 11 ...
## $ ybox : int 8 12 11 11 1 11 2 1 2 15 ...
## $ width : int 3 3 6 6 3 5 5 3 4 13 ...
## $ height: int 5 7 8 6 1 8 4 2 4 9 ...
## $ onpix : int 1 2 6 3 1 3 4 1 2 7 ...
## $ xbar : int 8 10 10 5 8 8 8 8 10 13 ...
## $ ybar : int 13 5 6 9 6 8 7 2 6 2 ...
## $ x2bar : int 0 5 2 4 6 6 6 2 2 6 ...
## $ y2bar : int 6 4 6 6 6 9 6 2 6 2 ...
## $ xybar : int 6 13 10 4 6 5 7 8 12 12 ...
## $ x2ybar: int 10 3 3 4 5 6 6 2 4 1 ...
## $ xy2bar: int 8 9 7 10 9 6 6 8 8 9 ...
## $ xedge : int 0 2 3 6 1 0 2 1 1 8 ...
## $ xedgey: int 8 8 7 10 7 8 8 6 6 1 ...
## $ yedge : int 0 4 3 2 5 9 7 2 1 1 ...
## $ yedgex: int 8 10 9 8 10 7 10 7 7 8 ...letter_train <-letter[1:1800, ]
letter_test <-letter[1801:2000, ]
letter_classifier <- ksvm(letter ~ ., data = letter_train, kernel = "vanilladot")## Setting default kernel parameterssummary(letter_classifier)## Length Class Mode
## 1 ksvm S4letter_predictions <- predict(letter_classifier, letter_test)
(p1 <- table(letter_predictions,letter_test$letter))##
## letter_predictions A B C D E F G H I J K L M N O P Q R
## A 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## B 0 10 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## C 0 0 11 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0
## D 0 0 0 4 0 0 1 0 2 0 0 0 0 0 0 0 0 0
## E 0 0 1 0 8 0 0 0 0 0 1 0 0 0 0 0 0 0
## F 0 0 0 0 0 4 0 0 0 1 0 0 0 0 0 0 0 0
## G 0 0 0 0 0 0 7 0 1 0 0 0 0 0 0 0 1 0
## H 0 0 0 1 0 0 0 6 0 0 0 1 0 0 0 0 0 1
## I 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0
## J 0 0 0 0 0 1 0 0 0 4 0 0 0 0 0 0 0 0
## K 0 0 0 0 0 0 0 1 0 0 5 0 0 0 0 0 0 0
## L 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0
## M 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0
## N 0 0 0 1 0 0 0 0 0 0 0 0 0 8 0 0 0 0
## O 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0
## P 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0
## Q 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 5 0
## R 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
## S 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## T 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## U 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## V 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## W 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0
## X 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0
## Y 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Z 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## letter_predictions S T U V W X Y Z
## A 0 0 0 0 1 0 1 0
## B 1 1 0 0 0 0 0 0
## C 0 0 0 0 0 0 0 0
## D 0 0 0 0 0 0 0 0
## E 0 0 0 0 0 0 0 0
## F 0 0 0 0 0 0 0 0
## G 0 0 0 0 0 0 0 0
## H 0 0 0 0 1 0 0 0
## I 0 0 0 0 0 0 0 0
## J 0 0 0 0 0 0 0 0
## K 0 1 0 0 0 0 0 0
## L 0 0 0 0 0 0 0 0
## M 0 0 0 0 0 0 0 0
## N 0 0 0 0 0 0 0 0
## O 0 0 0 0 0 0 0 0
## P 0 0 0 0 0 0 0 0
## Q 0 0 0 0 0 0 0 0
## R 0 0 0 0 0 0 0 0
## S 6 0 0 0 0 0 0 1
## T 0 5 0 0 0 0 0 0
## U 0 0 8 0 0 0 0 0
## V 0 0 0 14 0 0 0 0
## W 0 0 0 0 5 0 0 0
## X 0 0 0 0 0 8 0 0
## Y 0 1 0 0 0 0 7 0
## Z 0 0 0 0 0 0 0 4(Accuracy1 <- sum(diag(p1))/sum(p1)*100)## [1] 84library(rpart)
library(rpart.plot)
wine <- read.csv("~/whitewines.csv")
str(wine)## 'data.frame': 4898 obs. of 12 variables:
## $ fixed.acidity : num 6.7 5.7 5.9 5.3 6.4 7 7.9 6.6 7 6.5 ...
## $ volatile.acidity : num 0.62 0.22 0.19 0.47 0.29 0.14 0.12 0.38 0.16 0.37 ...
## $ citric.acid : num 0.24 0.2 0.26 0.1 0.21 0.41 0.49 0.28 0.3 0.33 ...
## $ residual.sugar : num 1.1 16 7.4 1.3 9.65 0.9 5.2 2.8 2.6 3.9 ...
## $ chlorides : num 0.039 0.044 0.034 0.036 0.041 0.037 0.049 0.043 0.043 0.027 ...
## $ free.sulfur.dioxide : num 6 41 33 11 36 22 33 17 34 40 ...
## $ total.sulfur.dioxide: num 62 113 123 74 119 95 152 67 90 130 ...
## $ density : num 0.993 0.999 0.995 0.991 0.993 ...
## $ pH : num 3.41 3.22 3.49 3.48 2.99 3.25 3.18 3.21 2.88 3.28 ...
## $ sulphates : num 0.32 0.46 0.42 0.54 0.34 0.43 0.47 0.47 0.47 0.39 ...
## $ alcohol : num 10.4 8.9 10.1 11.2 10.9 ...
## $ quality : int 5 6 6 4 6 6 6 6 6 7 ...wine_train <-wine[1:3750, ]
wine_test <-wine[3751:4898, ]
wine_model <- rpart(quality ~ ., data=wine_train)
summary(wine_model)## Call:
## rpart(formula = quality ~ ., data = wine_train)
## n= 3750
##
## CP nsplit rel error xerror xstd
## 1 0.15501053 0 1.0000000 1.0005679 0.02445814
## 2 0.05098911 1 0.8449895 0.8459873 0.02335127
## 3 0.02796998 2 0.7940004 0.8005963 0.02271109
## 4 0.01970128 3 0.7660304 0.7849093 0.02204877
## 5 0.01265926 4 0.7463291 0.7645262 0.02116044
## 6 0.01007193 5 0.7336698 0.7601898 0.02094564
## 7 0.01000000 6 0.7235979 0.7545736 0.02080176
##
## Variable importance
## alcohol density volatile.acidity
## 34 21 15
## chlorides total.sulfur.dioxide free.sulfur.dioxide
## 11 7 6
## residual.sugar sulphates citric.acid
## 3 1 1
##
## Node number 1: 3750 observations, complexity param=0.1550105
## mean=5.870933, MSE=0.7854751
## left son=2 (2372 obs) right son=3 (1378 obs)
## Primary splits:
## alcohol < 10.85 to the left, improve=0.15501050, (0 missing)
## density < 0.992035 to the right, improve=0.10915940, (0 missing)
## chlorides < 0.0395 to the right, improve=0.07682258, (0 missing)
## total.sulfur.dioxide < 158.5 to the right, improve=0.04089663, (0 missing)
## citric.acid < 0.235 to the left, improve=0.03636458, (0 missing)
## Surrogate splits:
## density < 0.991995 to the right, agree=0.869, adj=0.644, (0 split)
## chlorides < 0.0375 to the right, agree=0.757, adj=0.339, (0 split)
## total.sulfur.dioxide < 103.5 to the right, agree=0.690, adj=0.155, (0 split)
## residual.sugar < 5.375 to the right, agree=0.667, adj=0.094, (0 split)
## sulphates < 0.345 to the right, agree=0.647, adj=0.038, (0 split)
##
## Node number 2: 2372 observations, complexity param=0.05098911
## mean=5.604975, MSE=0.5981709
## left son=4 (1611 obs) right son=5 (761 obs)
## Primary splits:
## volatile.acidity < 0.2275 to the right, improve=0.10585250, (0 missing)
## free.sulfur.dioxide < 13.5 to the left, improve=0.03390500, (0 missing)
## citric.acid < 0.235 to the left, improve=0.03204075, (0 missing)
## alcohol < 10.11667 to the left, improve=0.03136524, (0 missing)
## chlorides < 0.0585 to the right, improve=0.01633599, (0 missing)
## Surrogate splits:
## pH < 3.485 to the left, agree=0.694, adj=0.047, (0 split)
## sulphates < 0.755 to the left, agree=0.685, adj=0.020, (0 split)
## total.sulfur.dioxide < 105.5 to the right, agree=0.683, adj=0.011, (0 split)
## residual.sugar < 0.75 to the right, agree=0.681, adj=0.007, (0 split)
## chlorides < 0.0285 to the right, agree=0.680, adj=0.003, (0 split)
##
## Node number 3: 1378 observations, complexity param=0.02796998
## mean=6.328737, MSE=0.7765472
## left son=6 (84 obs) right son=7 (1294 obs)
## Primary splits:
## free.sulfur.dioxide < 10.5 to the left, improve=0.07699080, (0 missing)
## alcohol < 11.76667 to the left, improve=0.06210660, (0 missing)
## total.sulfur.dioxide < 67.5 to the left, improve=0.04438619, (0 missing)
## residual.sugar < 1.375 to the left, improve=0.02905351, (0 missing)
## fixed.acidity < 7.35 to the right, improve=0.02613259, (0 missing)
## Surrogate splits:
## total.sulfur.dioxide < 53.5 to the left, agree=0.952, adj=0.214, (0 split)
## volatile.acidity < 0.875 to the right, agree=0.940, adj=0.024, (0 split)
##
## Node number 4: 1611 observations, complexity param=0.01265926
## mean=5.43203, MSE=0.5098121
## left son=8 (688 obs) right son=9 (923 obs)
## Primary splits:
## volatile.acidity < 0.3025 to the right, improve=0.04540111, (0 missing)
## alcohol < 10.05 to the left, improve=0.03874403, (0 missing)
## free.sulfur.dioxide < 13.5 to the left, improve=0.03338886, (0 missing)
## chlorides < 0.0495 to the right, improve=0.02574623, (0 missing)
## citric.acid < 0.195 to the left, improve=0.02327981, (0 missing)
## Surrogate splits:
## citric.acid < 0.215 to the left, agree=0.633, adj=0.141, (0 split)
## free.sulfur.dioxide < 20.5 to the left, agree=0.600, adj=0.063, (0 split)
## chlorides < 0.0595 to the right, agree=0.593, adj=0.047, (0 split)
## residual.sugar < 1.15 to the left, agree=0.583, adj=0.023, (0 split)
## total.sulfur.dioxide < 219.25 to the right, agree=0.582, adj=0.022, (0 split)
##
## Node number 5: 761 observations
## mean=5.971091, MSE=0.5878633
##
## Node number 6: 84 observations
## mean=5.369048, MSE=1.137613
##
## Node number 7: 1294 observations, complexity param=0.01970128
## mean=6.391036, MSE=0.6894405
## left son=14 (629 obs) right son=15 (665 obs)
## Primary splits:
## alcohol < 11.76667 to the left, improve=0.06504696, (0 missing)
## chlorides < 0.0395 to the right, improve=0.02758705, (0 missing)
## fixed.acidity < 7.35 to the right, improve=0.02750932, (0 missing)
## pH < 3.055 to the left, improve=0.02307356, (0 missing)
## total.sulfur.dioxide < 191.5 to the right, improve=0.02186818, (0 missing)
## Surrogate splits:
## density < 0.990885 to the right, agree=0.720, adj=0.424, (0 split)
## volatile.acidity < 0.2675 to the left, agree=0.637, adj=0.253, (0 split)
## chlorides < 0.0365 to the right, agree=0.630, adj=0.238, (0 split)
## residual.sugar < 1.475 to the left, agree=0.575, adj=0.126, (0 split)
## total.sulfur.dioxide < 128.5 to the right, agree=0.574, adj=0.124, (0 split)
##
## Node number 8: 688 observations
## mean=5.255814, MSE=0.4054895
##
## Node number 9: 923 observations
## mean=5.56338, MSE=0.5471747
##
## Node number 14: 629 observations, complexity param=0.01007193
## mean=6.173291, MSE=0.6838017
## left son=28 (11 obs) right son=29 (618 obs)
## Primary splits:
## volatile.acidity < 0.465 to the right, improve=0.06897561, (0 missing)
## total.sulfur.dioxide < 200 to the right, improve=0.04223066, (0 missing)
## residual.sugar < 0.975 to the left, improve=0.03061714, (0 missing)
## fixed.acidity < 7.35 to the right, improve=0.02978501, (0 missing)
## sulphates < 0.575 to the left, improve=0.02165970, (0 missing)
## Surrogate splits:
## citric.acid < 0.045 to the left, agree=0.986, adj=0.182, (0 split)
## total.sulfur.dioxide < 279.25 to the right, agree=0.986, adj=0.182, (0 split)
##
## Node number 15: 665 observations
## mean=6.596992, MSE=0.6075098
##
## Node number 28: 11 observations
## mean=4.545455, MSE=0.9752066
##
## Node number 29: 618 observations
## mean=6.202265, MSE=0.6306098rpart.plot(wine_model, digits=3, type=1)
wine_predict <- predict(wine_model, wine_test)
summary(wine_test$quality) ## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 5.000 6.000 5.901 6.000 9.000cor(wine_predict, wine_test$quality)## [1] 0.5369525news <- read.csv("~/OnlineNewsPopularity.csv")
str(news)## 'data.frame': 39644 obs. of 61 variables:
## $ url : Factor w/ 39644 levels "http://mashable.com/2013/01/07/amazon-instant-video-browser/",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ timedelta : num 731 731 731 731 731 731 731 731 731 731 ...
## $ n_tokens_title : num 12 9 9 9 13 10 8 12 11 10 ...
## $ n_tokens_content : num 219 255 211 531 1072 ...
## $ n_unique_tokens : num 0.664 0.605 0.575 0.504 0.416 ...
## $ n_non_stop_words : num 1 1 1 1 1 ...
## $ n_non_stop_unique_tokens : num 0.815 0.792 0.664 0.666 0.541 ...
## $ num_hrefs : num 4 3 3 9 19 2 21 20 2 4 ...
## $ num_self_hrefs : num 2 1 1 0 19 2 20 20 0 1 ...
## $ num_imgs : num 1 1 1 1 20 0 20 20 0 1 ...
## $ num_videos : num 0 0 0 0 0 0 0 0 0 1 ...
## $ average_token_length : num 4.68 4.91 4.39 4.4 4.68 ...
## $ num_keywords : num 5 4 6 7 7 9 10 9 7 5 ...
## $ data_channel_is_lifestyle : num 0 0 0 0 0 0 1 0 0 0 ...
## $ data_channel_is_entertainment: num 1 0 0 1 0 0 0 0 0 0 ...
## $ data_channel_is_bus : num 0 1 1 0 0 0 0 0 0 0 ...
## $ data_channel_is_socmed : num 0 0 0 0 0 0 0 0 0 0 ...
## $ data_channel_is_tech : num 0 0 0 0 1 1 0 1 1 0 ...
## $ data_channel_is_world : num 0 0 0 0 0 0 0 0 0 1 ...
## $ kw_min_min : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_max_min : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_avg_min : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_min_max : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_max_max : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_avg_max : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_min_avg : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_max_avg : num 0 0 0 0 0 0 0 0 0 0 ...
## $ kw_avg_avg : num 0 0 0 0 0 0 0 0 0 0 ...
## $ self_reference_min_shares : num 496 0 918 0 545 8500 545 545 0 0 ...
## $ self_reference_max_shares : num 496 0 918 0 16000 8500 16000 16000 0 0 ...
## $ self_reference_avg_sharess : num 496 0 918 0 3151 ...
## $ weekday_is_monday : num 1 1 1 1 1 1 1 1 1 1 ...
## $ weekday_is_tuesday : num 0 0 0 0 0 0 0 0 0 0 ...
## $ weekday_is_wednesday : num 0 0 0 0 0 0 0 0 0 0 ...
## $ weekday_is_thursday : num 0 0 0 0 0 0 0 0 0 0 ...
## $ weekday_is_friday : num 0 0 0 0 0 0 0 0 0 0 ...
## $ weekday_is_saturday : num 0 0 0 0 0 0 0 0 0 0 ...
## $ weekday_is_sunday : num 0 0 0 0 0 0 0 0 0 0 ...
## $ is_weekend : num 0 0 0 0 0 0 0 0 0 0 ...
## $ LDA_00 : num 0.5003 0.7998 0.2178 0.0286 0.0286 ...
## $ LDA_01 : num 0.3783 0.05 0.0333 0.4193 0.0288 ...
## $ LDA_02 : num 0.04 0.0501 0.0334 0.4947 0.0286 ...
## $ LDA_03 : num 0.0413 0.0501 0.0333 0.0289 0.0286 ...
## $ LDA_04 : num 0.0401 0.05 0.6822 0.0286 0.8854 ...
## $ global_subjectivity : num 0.522 0.341 0.702 0.43 0.514 ...
## $ global_sentiment_polarity : num 0.0926 0.1489 0.3233 0.1007 0.281 ...
## $ global_rate_positive_words : num 0.0457 0.0431 0.0569 0.0414 0.0746 ...
## $ global_rate_negative_words : num 0.0137 0.01569 0.00948 0.02072 0.01213 ...
## $ rate_positive_words : num 0.769 0.733 0.857 0.667 0.86 ...
## $ rate_negative_words : num 0.231 0.267 0.143 0.333 0.14 ...
## $ avg_positive_polarity : num 0.379 0.287 0.496 0.386 0.411 ...
## $ min_positive_polarity : num 0.1 0.0333 0.1 0.1364 0.0333 ...
## $ max_positive_polarity : num 0.7 0.7 1 0.8 1 0.6 1 1 0.8 0.5 ...
## $ avg_negative_polarity : num -0.35 -0.119 -0.467 -0.37 -0.22 ...
## $ min_negative_polarity : num -0.6 -0.125 -0.8 -0.6 -0.5 -0.4 -0.5 -0.5 -0.125 -0.5 ...
## $ max_negative_polarity : num -0.2 -0.1 -0.133 -0.167 -0.05 ...
## $ title_subjectivity : num 0.5 0 0 0 0.455 ...
## $ title_sentiment_polarity : num -0.188 0 0 0 0.136 ...
## $ abs_title_subjectivity : num 0 0.5 0.5 0.5 0.0455 ...
## $ abs_title_sentiment_polarity : num 0.188 0 0 0 0.136 ...
## $ shares : int 593 711 1500 1200 505 855 556 891 3600 710 ...newsShort <- data.frame(news$n_tokens_title, news$n_tokens_content, news$n_unique_tokens, news$n_non_stop_words, news$num_hrefs, news$num_imgs, news$num_videos, news$average_token_length, news$num_keywords, news$kw_max_max, news$global_sentiment_polarity, news$avg_positive_polarity, news$title_subjectivity, news$title_sentiment_polarity, news$abs_title_subjectivity, news$abs_title_sentiment_polarity, news$shares)
colnames(newsShort) <- c("n_tokens_title", "n_tokens_content", "n_unique_tokens", "n_non_stop_words", "num_hrefs", "num_imgs", "num_videos", "average_token_length", "num_keywords", "kw_max_max", "global_sentiment_polarity", "avg_positive_polarity", "title_subjectivity", "title_sentiment_polarity", "abs_title_subjectivity", "abs_title_sentiment_polarity", "shares")
newsShort$popular = rep('na', nrow(newsShort))
for(i in 1:39644) {
if(newsShort$shares[i] >= 1400) {
newsShort$popular[i] = "yes"}
else {newsShort$popular[i] = "no"}
}
newsShort$shares = newsShort$popular
newsShort$shares <- as.factor(newsShort$shares)
set.seed(12345)
news_rand <- newsShort[order(runif(10000)), ]
news_train <- news_rand[1:9000, ]
news_test <- news_rand[9001:10000, ]
prop.table(table(news_train$shares))##
## no yes
## 0.4308889 0.5691111prop.table(table(news_test$shares))##
## no yes
## 0.414 0.586news_model <- C5.0(news_train[-17], news_train$shares)
summary(news_model)##
## Call:
## C5.0.default(x = news_train[-17], y = news_train$shares)
##
##
## C5.0 [Release 2.07 GPL Edition] Sat Jun 30 16:38:14 2018
## -------------------------------
##
## Class specified by attribute `outcome'
##
## Read 9000 cases (18 attributes) from undefined.data
##
## Decision tree:
##
## popular = no: no (3878)
## popular = yes: yes (5122)
##
##
## Evaluation on training data (9000 cases):
##
## Decision Tree
## ----------------
## Size Errors
##
## 2 0( 0.0%) <<
##
##
## (a) (b) <-classified as
## ---- ----
## 3878 (a): class no
## 5122 (b): class yes
##
##
## Attribute usage:
##
## 100.00% popular
##
##
## Time: 0.1 secsnews_pred <- predict(news_model, news_test)
(p3 <- table(news_pred, news_test$shares))##
## news_pred no yes
## no 414 0
## yes 0 586(Accuracy3 <- sum(diag(p3))/sum(p3)*100)## [1] 100library(kernlab)
str(newsShort)## 'data.frame': 39644 obs. of 18 variables:
## $ n_tokens_title : num 12 9 9 9 13 10 8 12 11 10 ...
## $ n_tokens_content : num 219 255 211 531 1072 ...
## $ n_unique_tokens : num 0.664 0.605 0.575 0.504 0.416 ...
## $ n_non_stop_words : num 1 1 1 1 1 ...
## $ num_hrefs : num 4 3 3 9 19 2 21 20 2 4 ...
## $ num_imgs : num 1 1 1 1 20 0 20 20 0 1 ...
## $ num_videos : num 0 0 0 0 0 0 0 0 0 1 ...
## $ average_token_length : num 4.68 4.91 4.39 4.4 4.68 ...
## $ num_keywords : num 5 4 6 7 7 9 10 9 7 5 ...
## $ kw_max_max : num 0 0 0 0 0 0 0 0 0 0 ...
## $ global_sentiment_polarity : num 0.0926 0.1489 0.3233 0.1007 0.281 ...
## $ avg_positive_polarity : num 0.379 0.287 0.496 0.386 0.411 ...
## $ title_subjectivity : num 0.5 0 0 0 0.455 ...
## $ title_sentiment_polarity : num -0.188 0 0 0 0.136 ...
## $ abs_title_subjectivity : num 0 0.5 0.5 0.5 0.0455 ...
## $ abs_title_sentiment_polarity: num 0.188 0 0 0 0.136 ...
## $ shares : Factor w/ 2 levels "no","yes": 1 1 2 1 1 1 1 1 2 1 ...
## $ popular : chr "no" "no" "yes" "no" ...news_train1<- newsShort[1:31500, ]
news_test1 <-newsShort[31501:39644, ]
prop.table(table(news_train1$shares))##
## no yes
## 0.4525714 0.5474286prop.table(table(news_test1$shares))##
## no yes
## 0.5198919 0.4801081news_model1 <- ksvm(shares ~ ., data = news_train1, kernel = "vanilladot")## Setting default kernel parameterssummary(news_model1)## Length Class Mode
## 1 ksvm S4news_predi1 <-predict(news_model1, news_test1)
(p4 <-table(news_predi1, news_test1$shares))##
## news_predi1 no yes
## no 4234 0
## yes 0 3910(Accuracy4 <-sum(diag(p4))/sum(p4)*100)## [1] 100library(ggplot2)##
## Attaching package: 'ggplot2'## The following object is masked from 'package:kernlab':
##
## alphalibrary(rpart)
library(rpart.plot)
str(newsShort)## 'data.frame': 39644 obs. of 18 variables:
## $ n_tokens_title : num 12 9 9 9 13 10 8 12 11 10 ...
## $ n_tokens_content : num 219 255 211 531 1072 ...
## $ n_unique_tokens : num 0.664 0.605 0.575 0.504 0.416 ...
## $ n_non_stop_words : num 1 1 1 1 1 ...
## $ num_hrefs : num 4 3 3 9 19 2 21 20 2 4 ...
## $ num_imgs : num 1 1 1 1 20 0 20 20 0 1 ...
## $ num_videos : num 0 0 0 0 0 0 0 0 0 1 ...
## $ average_token_length : num 4.68 4.91 4.39 4.4 4.68 ...
## $ num_keywords : num 5 4 6 7 7 9 10 9 7 5 ...
## $ kw_max_max : num 0 0 0 0 0 0 0 0 0 0 ...
## $ global_sentiment_polarity : num 0.0926 0.1489 0.3233 0.1007 0.281 ...
## $ avg_positive_polarity : num 0.379 0.287 0.496 0.386 0.411 ...
## $ title_subjectivity : num 0.5 0 0 0 0.455 ...
## $ title_sentiment_polarity : num -0.188 0 0 0 0.136 ...
## $ abs_title_subjectivity : num 0 0.5 0.5 0.5 0.0455 ...
## $ abs_title_sentiment_polarity: num 0.188 0 0 0 0.136 ...
## $ shares : Factor w/ 2 levels "no","yes": 1 1 2 1 1 1 1 1 2 1 ...
## $ popular : chr "no" "no" "yes" "no" ...news_train2<- newsShort[1:31500, ]
news_test2 <-newsShort[31501:39644, ]
news.rpart <-rpart(shares~., data=news_train2)
summary(news.rpart)## Call:
## rpart(formula = shares ~ ., data = news_train2)
## n= 31500
##
## CP nsplit rel error xerror xstd
## 1 1.00 0 1 1 0.006196763
## 2 0.01 1 0 0 0.000000000
##
## Variable importance
## popular global_sentiment_polarity
## 97 1
## num_keywords
## 1
##
## Node number 1: 31500 observations, complexity param=1
## predicted class=yes expected loss=0.4525714 P(node) =1
## class counts: 14256 17244
## probabilities: 0.453 0.547
## left son=2 (14256 obs) right son=3 (17244 obs)
## Primary splits:
## popular splits as LR, improve=15608.28000, (0 missing)
## num_imgs < 2.5 to the left, improve= 150.03170, (0 missing)
## num_hrefs < 14.5 to the left, improve= 149.08260, (0 missing)
## n_unique_tokens < 0.4396818 to the right, improve= 81.93555, (0 missing)
## global_sentiment_polarity < 0.09769614 to the left, improve= 79.84962, (0 missing)
## Surrogate splits:
## global_sentiment_polarity < 0.05648387 to the left, agree=0.554, adj=0.015, (0 split)
## num_keywords < 4.5 to the left, agree=0.552, adj=0.010, (0 split)
## num_videos < 24.5 to the right, agree=0.548, adj=0.002, (0 split)
## n_unique_tokens < 0.7555555 to the right, agree=0.548, adj=0.002, (0 split)
## kw_max_max < 22950 to the left, agree=0.548, adj=0.002, (0 split)
##
## Node number 2: 14256 observations
## predicted class=no expected loss=0 P(node) =0.4525714
## class counts: 14256 0
## probabilities: 1.000 0.000
##
## Node number 3: 17244 observations
## predicted class=yes expected loss=0 P(node) =0.5474286
## class counts: 0 17244
## probabilities: 0.000 1.000rpart.plot(news.rpart, digits=3, type=1)
(news_predi2 <-predict(news.rpart, news_test2 ))## no yes
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## 39644 1 0summary(news_predi2)## no yes
## Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000
## Mean :0.5199 Mean :0.4801
## 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000summary(news_test2$shares)## no yes
## 4234 3910# We created a model using the rpart package. Our visualization results indicate that 45% of the time shares were not based on popularity of the news but 55% of the time news was shared because it was popular.Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.