Decision Tree with R
Model (packages = party, rpart)
MD. Mehedi Hassan Galib
27 July, 2020
Loading dataset
data <- read.csv("C:/Users/Mehedi Hassan Galib/Desktop/R/Cardiotocographic.csv")
Preparing the dataset
Structure of the dataset
str(data)## 'data.frame': 2126 obs. of 22 variables:
## $ LB : int 120 132 133 134 132 134 134 122 122 122 ...
## $ AC : num 0 0.00638 0.00332 0.00256 0.00651 ...
## $ FM : num 0 0 0 0 0 0 0 0 0 0 ...
## $ UC : num 0 0.00638 0.00831 0.00768 0.00814 ...
## $ DL : num 0 0.00319 0.00332 0.00256 0 ...
## $ DS : num 0 0 0 0 0 0 0 0 0 0 ...
## $ DP : num 0 0 0 0 0 ...
## $ ASTV : int 73 17 16 16 16 26 29 83 84 86 ...
## $ MSTV : num 0.5 2.1 2.1 2.4 2.4 5.9 6.3 0.5 0.5 0.3 ...
## $ ALTV : int 43 0 0 0 0 0 0 6 5 6 ...
## $ MLTV : num 2.4 10.4 13.4 23 19.9 0 0 15.6 13.6 10.6 ...
## $ Width : int 64 130 130 117 117 150 150 68 68 68 ...
## $ Min : int 62 68 68 53 53 50 50 62 62 62 ...
## $ Max : int 126 198 198 170 170 200 200 130 130 130 ...
## $ Nmax : int 2 6 5 11 9 5 6 0 0 1 ...
## $ Nzeros : int 0 1 1 0 0 3 3 0 0 0 ...
## $ Mode : int 120 141 141 137 137 76 71 122 122 122 ...
## $ Mean : int 137 136 135 134 136 107 107 122 122 122 ...
## $ Median : int 121 140 138 137 138 107 106 123 123 123 ...
## $ Variance: int 73 12 13 13 11 170 215 3 3 1 ...
## $ Tendency: int 1 0 0 1 1 0 0 1 1 1 ...
## $ NSP : int 2 1 1 1 1 3 3 3 3 3 ...
Converting a variable into Factor and making a new column
data$NSPF <- factor(data$NSP)
Partitioning (split data into training and testing
Training data - 80%
Testing data - 20%
No number after comma means all columns will be included
set.seed(1234)
p_data <- sample(2, nrow(data), replace = TRUE, prob = c(0.8, 0.2))
train <- data[p_data==1,]
test <- data[p_data==2,]
Loading necessary package
library(party)## Warning: package 'party' was built under R version 4.0.2## Loading required package: grid## Loading required package: mvtnorm## Loading required package: modeltools## Loading required package: stats4## Loading required package: strucchange## Warning: package 'strucchange' was built under R version 4.0.2## Loading required package: zoo## Warning: package 'zoo' was built under R version 4.0.2##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Loading required package: sandwich## Warning: package 'sandwich' was built under R version 4.0.2
Decision Tree (party)
tree <- ctree(NSPF~LB+AC+FM, data = train)
tree##
## Conditional inference tree with 10 terminal nodes
##
## Response: NSPF
## Inputs: LB, AC, FM
## Number of observations: 1718
##
## 1) AC <= 0.000834028; criterion = 1, statistic = 263.403
## 2) LB <= 136; criterion = 1, statistic = 131.511
## 3) FM <= 0.111898; criterion = 1, statistic = 35.729
## 4)* weights = 405
## 3) FM > 0.111898
## 5)* weights = 11
## 2) LB > 136
## 6)* weights = 314
## 1) AC > 0.000834028
## 7) AC <= 0.002209945; criterion = 1, statistic = 52.155
## 8) LB <= 136; criterion = 0.999, statistic = 17.292
## 9) FM <= 0.0121396; criterion = 1, statistic = 42.826
## 10)* weights = 103
## 9) FM > 0.0121396
## 11)* weights = 7
## 8) LB > 136
## 12)* weights = 78
## 7) AC > 0.002209945
## 13) LB <= 110; criterion = 1, statistic = 18.889
## 14)* weights = 18
## 13) LB > 110
## 15) LB <= 147; criterion = 0.965, statistic = 8.877
## 16) FM <= 0.2354892; criterion = 0.986, statistic = 10.725
## 17)* weights = 742
## 16) FM > 0.2354892
## 18)* weights = 12
## 15) LB > 147
## 19)* weights = 28
Plotting the Tree
plot(tree)
Customize the Tree
Confidence level - 99%
minsplit = 500 - the brach will be splitted only when it contains at least 500 data
tree <- ctree(NSPF~LB+AC+FM, data = train, controls = ctree_control(mincriterion = 0.999,
minsplit = 500))
tree##
## Conditional inference tree with 5 terminal nodes
##
## Response: NSPF
## Inputs: LB, AC, FM
## Number of observations: 1718
##
## 1) AC <= 0.000834028; criterion = 1, statistic = 263.403
## 2) LB <= 136; criterion = 1, statistic = 131.511
## 3)* weights = 416
## 2) LB > 136
## 4)* weights = 314
## 1) AC > 0.000834028
## 5) AC <= 0.002209945; criterion = 1, statistic = 52.155
## 6)* weights = 188
## 5) AC > 0.002209945
## 7) LB <= 110; criterion = 1, statistic = 18.889
## 8)* weights = 18
## 7) LB > 110
## 9)* weights = 782plot(tree)
Probability of the test dataset (values of explanatory variables)
based on tree, applied on test
Predict(tree,test,type="prob")## [[1]]
## [1] 0.985933504 0.007672634 0.006393862
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## [1] 0.985933504 0.007672634 0.006393862
##
## [[209]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[210]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[211]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[212]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[213]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[214]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[215]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[216]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[217]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[218]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[219]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[220]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[221]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[222]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[223]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[224]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[225]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[226]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[227]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[228]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[229]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[230]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[231]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[232]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[233]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[234]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[235]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[236]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[237]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[238]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[239]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[240]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[241]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[242]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[243]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[244]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[245]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[246]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[247]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[248]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[249]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[250]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[251]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[252]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[253]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[254]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[255]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[256]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[257]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[258]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[259]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[260]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[261]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[262]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[263]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[264]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[265]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[266]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[267]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[268]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[269]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[270]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[271]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[272]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[273]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[274]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[275]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[276]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[277]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[278]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[279]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[280]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[281]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[282]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[283]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[284]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[285]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[286]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[287]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[288]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[289]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[290]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[291]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[292]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[293]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[294]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[295]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[296]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[297]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[298]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[299]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[300]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[301]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[302]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[303]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[304]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[305]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[306]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[307]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[308]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[309]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[310]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[311]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[312]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[313]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[314]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[315]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[316]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[317]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[318]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[319]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[320]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[321]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[322]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[323]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[324]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[325]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[326]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[327]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[328]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[329]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[330]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[331]]
## [1] 0.7222222 0.0000000 0.2777778
##
## [[332]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[333]]
## [1] 0.7222222 0.0000000 0.2777778
##
## [[334]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[335]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[336]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[337]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[338]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[339]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[340]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[341]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[342]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[343]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[344]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[345]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[346]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[347]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[348]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[349]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[350]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[351]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[352]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[353]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[354]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[355]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[356]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[357]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[358]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[359]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[360]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[361]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[362]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[363]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[364]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[365]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[366]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[367]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[368]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[369]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[370]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[371]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[372]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[373]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[374]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[375]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[376]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[377]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[378]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[379]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[380]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[381]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[382]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[383]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[384]]
## [1] 0.79787234 0.14361702 0.05851064
##
## [[385]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[386]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[387]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[388]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[389]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[390]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[391]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[392]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[393]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[394]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[395]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[396]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[397]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[398]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[399]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[400]]
## [1] 0.985933504 0.007672634 0.006393862
##
## [[401]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[402]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[403]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[404]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[405]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[406]]
## [1] 0.69230769 0.08894231 0.21875000
##
## [[407]]
## [1] 0.4012739 0.4968153 0.1019108
##
## [[408]]
## [1] 0.4012739 0.4968153 0.1019108
Probability of the test dataset (values of response variable)
predict(tree,test)## [1] 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
## [38] 2 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 2 2 2 2 2 2 2 1 2 2 2 2 1 1 1 2 2 1 1
## [75] 2 2 2 1 2 2 1 2 2 1 2 2 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 2 1 2 2 2 2 2 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 2 1 1 1 2 1 1 1 1
## [149] 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 2 2 1 2 2 2 2 1 2 1 2 2 1 1 1 1 1
## [186] 1 1 1 1 2 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [260] 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 2 1 2 1 1 1 2 1 1 1 1 2 2 2 2 2
## [297] 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 2 2 2 1 1 1 1 1 1 2 1 1 1 1 1 1 2
## [371] 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
## [408] 2
## Levels: 1 2 3
Decision Tree (rpart)
Loading necessary package
library(rpart)## Warning: package 'rpart' was built under R version 4.0.2library(rpart.plot)## Warning: package 'rpart.plot' was built under R version 4.0.2
Decision Tree
tree1 <- rpart(NSPF~LB+AC+FM, train)
tree1## n= 1718
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1718 370 1 (0.78463329 0.13154831 0.08381839)
## 2) AC>=0.000838599 988 54 1 (0.94534413 0.03340081 0.02125506) *
## 3) AC< 0.000838599 730 316 1 (0.56712329 0.26438356 0.16849315)
## 6) LB< 136.5 416 128 1 (0.69230769 0.08894231 0.21875000)
## 12) FM< 0.2049286 405 117 1 (0.71111111 0.09135802 0.19753086) *
## 13) FM>=0.2049286 11 0 3 (0.00000000 0.00000000 1.00000000) *
## 7) LB>=136.5 314 158 2 (0.40127389 0.49681529 0.10191083)
## 14) FM< 0.001300498 204 102 1 (0.50000000 0.46568627 0.03431373)
## 28) LB< 144.5 99 34 1 (0.65656566 0.33333333 0.01010101) *
## 29) LB>=144.5 105 43 2 (0.35238095 0.59047619 0.05714286) *
## 15) FM>=0.001300498 110 49 2 (0.21818182 0.55454545 0.22727273)
## 30) LB< 147 89 35 2 (0.25842697 0.60674157 0.13483146) *
## 31) LB>=147 21 8 3 (0.04761905 0.33333333 0.61904762) *rpart.plot(tree1)
For more info
tree1 <- rpart(NSPF~LB+AC+FM, train)
tree1## n= 1718
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1718 370 1 (0.78463329 0.13154831 0.08381839)
## 2) AC>=0.000838599 988 54 1 (0.94534413 0.03340081 0.02125506) *
## 3) AC< 0.000838599 730 316 1 (0.56712329 0.26438356 0.16849315)
## 6) LB< 136.5 416 128 1 (0.69230769 0.08894231 0.21875000)
## 12) FM< 0.2049286 405 117 1 (0.71111111 0.09135802 0.19753086) *
## 13) FM>=0.2049286 11 0 3 (0.00000000 0.00000000 1.00000000) *
## 7) LB>=136.5 314 158 2 (0.40127389 0.49681529 0.10191083)
## 14) FM< 0.001300498 204 102 1 (0.50000000 0.46568627 0.03431373)
## 28) LB< 144.5 99 34 1 (0.65656566 0.33333333 0.01010101) *
## 29) LB>=144.5 105 43 2 (0.35238095 0.59047619 0.05714286) *
## 15) FM>=0.001300498 110 49 2 (0.21818182 0.55454545 0.22727273)
## 30) LB< 147 89 35 2 (0.25842697 0.60674157 0.13483146) *
## 31) LB>=147 21 8 3 (0.04761905 0.33333333 0.61904762) *rpart.plot(tree1,extra=1)
For more info
(934 out of 988 patients belong to group 1)
tree1 <- rpart(NSPF~LB+AC+FM, train)
tree1## n= 1718
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1718 370 1 (0.78463329 0.13154831 0.08381839)
## 2) AC>=0.000838599 988 54 1 (0.94534413 0.03340081 0.02125506) *
## 3) AC< 0.000838599 730 316 1 (0.56712329 0.26438356 0.16849315)
## 6) LB< 136.5 416 128 1 (0.69230769 0.08894231 0.21875000)
## 12) FM< 0.2049286 405 117 1 (0.71111111 0.09135802 0.19753086) *
## 13) FM>=0.2049286 11 0 3 (0.00000000 0.00000000 1.00000000) *
## 7) LB>=136.5 314 158 2 (0.40127389 0.49681529 0.10191083)
## 14) FM< 0.001300498 204 102 1 (0.50000000 0.46568627 0.03431373)
## 28) LB< 144.5 99 34 1 (0.65656566 0.33333333 0.01010101) *
## 29) LB>=144.5 105 43 2 (0.35238095 0.59047619 0.05714286) *
## 15) FM>=0.001300498 110 49 2 (0.21818182 0.55454545 0.22727273)
## 30) LB< 147 89 35 2 (0.25842697 0.60674157 0.13483146) *
## 31) LB>=147 21 8 3 (0.04761905 0.33333333 0.61904762) *rpart.plot(tree1,extra=2)
Probability of the test dataset (values of explanatory variables)
based on tree1, applied on test
predict(tree1,test)## 1 2 3
## 5 0.94534413 0.03340081 0.02125506
## 14 0.94534413 0.03340081 0.02125506
## 16 0.94534413 0.03340081 0.02125506
## 26 0.71111111 0.09135802 0.19753086
## 28 0.71111111 0.09135802 0.19753086
## 29 0.71111111 0.09135802 0.19753086
## 39 0.94534413 0.03340081 0.02125506
## 40 0.94534413 0.03340081 0.02125506
## 60 0.94534413 0.03340081 0.02125506
## 61 0.94534413 0.03340081 0.02125506
## 72 0.94534413 0.03340081 0.02125506
## 81 0.25842697 0.60674157 0.13483146
## 86 0.94534413 0.03340081 0.02125506
## 90 0.94534413 0.03340081 0.02125506
## 92 0.35238095 0.59047619 0.05714286
## 113 0.94534413 0.03340081 0.02125506
## 116 0.71111111 0.09135802 0.19753086
## 117 0.71111111 0.09135802 0.19753086
## 122 0.71111111 0.09135802 0.19753086
## 123 0.71111111 0.09135802 0.19753086
## 124 0.94534413 0.03340081 0.02125506
## 131 0.35238095 0.59047619 0.05714286
## 135 0.94534413 0.03340081 0.02125506
## 137 0.94534413 0.03340081 0.02125506
## 140 0.94534413 0.03340081 0.02125506
## 142 0.94534413 0.03340081 0.02125506
## 149 0.94534413 0.03340081 0.02125506
## 154 0.94534413 0.03340081 0.02125506
## 156 0.94534413 0.03340081 0.02125506
## 158 0.94534413 0.03340081 0.02125506
## 169 0.94534413 0.03340081 0.02125506
## 185 0.94534413 0.03340081 0.02125506
## 187 0.71111111 0.09135802 0.19753086
## 192 0.94534413 0.03340081 0.02125506
## 194 0.94534413 0.03340081 0.02125506
## 195 0.35238095 0.59047619 0.05714286
## 196 0.35238095 0.59047619 0.05714286
## 197 0.35238095 0.59047619 0.05714286
## 199 0.94534413 0.03340081 0.02125506
## 210 0.71111111 0.09135802 0.19753086
## 216 0.94534413 0.03340081 0.02125506
## 220 0.94534413 0.03340081 0.02125506
## 227 0.94534413 0.03340081 0.02125506
## 234 0.71111111 0.09135802 0.19753086
## 240 0.71111111 0.09135802 0.19753086
## 245 0.71111111 0.09135802 0.19753086
## 249 0.94534413 0.03340081 0.02125506
## 261 0.71111111 0.09135802 0.19753086
## 277 0.71111111 0.09135802 0.19753086
## 283 0.94534413 0.03340081 0.02125506
## 290 0.25842697 0.60674157 0.13483146
## 293 0.25842697 0.60674157 0.13483146
## 302 0.25842697 0.60674157 0.13483146
## 305 0.25842697 0.60674157 0.13483146
## 308 0.71111111 0.09135802 0.19753086
## 311 0.25842697 0.60674157 0.13483146
## 320 0.04761905 0.33333333 0.61904762
## 322 0.04761905 0.33333333 0.61904762
## 330 0.25842697 0.60674157 0.13483146
## 332 0.25842697 0.60674157 0.13483146
## 333 0.25842697 0.60674157 0.13483146
## 339 0.25842697 0.60674157 0.13483146
## 341 0.71111111 0.09135802 0.19753086
## 344 0.25842697 0.60674157 0.13483146
## 349 0.25842697 0.60674157 0.13483146
## 355 0.25842697 0.60674157 0.13483146
## 356 0.25842697 0.60674157 0.13483146
## 365 0.71111111 0.09135802 0.19753086
## 366 0.71111111 0.09135802 0.19753086
## 369 0.71111111 0.09135802 0.19753086
## 371 0.25842697 0.60674157 0.13483146
## 373 0.25842697 0.60674157 0.13483146
## 389 0.94534413 0.03340081 0.02125506
## 390 0.94534413 0.03340081 0.02125506
## 396 0.65656566 0.33333333 0.01010101
## 412 0.35238095 0.59047619 0.05714286
## 413 0.35238095 0.59047619 0.05714286
## 415 0.94534413 0.03340081 0.02125506
## 422 0.65656566 0.33333333 0.01010101
## 425 0.65656566 0.33333333 0.01010101
## 434 0.94534413 0.03340081 0.02125506
## 438 0.35238095 0.59047619 0.05714286
## 441 0.25842697 0.60674157 0.13483146
## 442 0.94534413 0.03340081 0.02125506
## 445 0.25842697 0.60674157 0.13483146
## 447 0.25842697 0.60674157 0.13483146
## 453 0.94534413 0.03340081 0.02125506
## 454 0.71111111 0.09135802 0.19753086
## 462 0.71111111 0.09135802 0.19753086
## 474 0.04761905 0.33333333 0.61904762
## 476 0.04761905 0.33333333 0.61904762
## 493 0.94534413 0.03340081 0.02125506
## 502 0.94534413 0.03340081 0.02125506
## 503 0.94534413 0.03340081 0.02125506
## 506 0.94534413 0.03340081 0.02125506
## 508 0.94534413 0.03340081 0.02125506
## 512 0.94534413 0.03340081 0.02125506
## 513 0.94534413 0.03340081 0.02125506
## 521 0.94534413 0.03340081 0.02125506
## 524 0.94534413 0.03340081 0.02125506
## 526 0.94534413 0.03340081 0.02125506
## 536 0.65656566 0.33333333 0.01010101
## 543 0.94534413 0.03340081 0.02125506
## 547 0.35238095 0.59047619 0.05714286
## 548 0.25842697 0.60674157 0.13483146
## 549 0.25842697 0.60674157 0.13483146
## 550 0.65656566 0.33333333 0.01010101
## 555 0.65656566 0.33333333 0.01010101
## 556 0.94534413 0.03340081 0.02125506
## 560 0.94534413 0.03340081 0.02125506
## 562 0.71111111 0.09135802 0.19753086
## 564 0.94534413 0.03340081 0.02125506
## 566 0.94534413 0.03340081 0.02125506
## 572 0.94534413 0.03340081 0.02125506
## 580 0.71111111 0.09135802 0.19753086
## 581 0.94534413 0.03340081 0.02125506
## 588 0.94534413 0.03340081 0.02125506
## 589 0.71111111 0.09135802 0.19753086
## 591 0.94534413 0.03340081 0.02125506
## 592 0.94534413 0.03340081 0.02125506
## 607 0.94534413 0.03340081 0.02125506
## 612 0.94534413 0.03340081 0.02125506
## 615 0.94534413 0.03340081 0.02125506
## 621 0.94534413 0.03340081 0.02125506
## 624 0.94534413 0.03340081 0.02125506
## 626 0.94534413 0.03340081 0.02125506
## 628 0.94534413 0.03340081 0.02125506
## 631 0.94534413 0.03340081 0.02125506
## 635 0.94534413 0.03340081 0.02125506
## 636 0.25842697 0.60674157 0.13483146
## 638 0.94534413 0.03340081 0.02125506
## 644 0.94534413 0.03340081 0.02125506
## 648 0.71111111 0.09135802 0.19753086
## 650 0.71111111 0.09135802 0.19753086
## 653 0.71111111 0.09135802 0.19753086
## 657 0.94534413 0.03340081 0.02125506
## 660 0.94534413 0.03340081 0.02125506
## 662 0.71111111 0.09135802 0.19753086
## 663 0.65656566 0.33333333 0.01010101
## 672 0.65656566 0.33333333 0.01010101
## 675 0.94534413 0.03340081 0.02125506
## 676 0.94534413 0.03340081 0.02125506
## 678 0.94534413 0.03340081 0.02125506
## 680 0.65656566 0.33333333 0.01010101
## 685 0.94534413 0.03340081 0.02125506
## 687 0.71111111 0.09135802 0.19753086
## 691 0.00000000 0.00000000 1.00000000
## 712 0.94534413 0.03340081 0.02125506
## 713 0.94534413 0.03340081 0.02125506
## 719 0.94534413 0.03340081 0.02125506
## 721 0.71111111 0.09135802 0.19753086
## 723 0.25842697 0.60674157 0.13483146
## 726 0.94534413 0.03340081 0.02125506
## 730 0.94534413 0.03340081 0.02125506
## 731 0.71111111 0.09135802 0.19753086
## 738 0.94534413 0.03340081 0.02125506
## 739 0.94534413 0.03340081 0.02125506
## 740 0.94534413 0.03340081 0.02125506
## 754 0.71111111 0.09135802 0.19753086
## 756 0.71111111 0.09135802 0.19753086
## 758 0.71111111 0.09135802 0.19753086
## 767 0.65656566 0.33333333 0.01010101
## 771 0.71111111 0.09135802 0.19753086
## 772 0.71111111 0.09135802 0.19753086
## 773 0.71111111 0.09135802 0.19753086
## 781 0.71111111 0.09135802 0.19753086
## 787 0.71111111 0.09135802 0.19753086
## 798 0.35238095 0.59047619 0.05714286
## 800 0.35238095 0.59047619 0.05714286
## 802 0.35238095 0.59047619 0.05714286
## 813 0.94534413 0.03340081 0.02125506
## 818 0.35238095 0.59047619 0.05714286
## 832 0.35238095 0.59047619 0.05714286
## 833 0.35238095 0.59047619 0.05714286
## 834 0.35238095 0.59047619 0.05714286
## 846 0.94534413 0.03340081 0.02125506
## 848 0.65656566 0.33333333 0.01010101
## 852 0.94534413 0.03340081 0.02125506
## 853 0.65656566 0.33333333 0.01010101
## 860 0.65656566 0.33333333 0.01010101
## 866 0.94534413 0.03340081 0.02125506
## 868 0.94534413 0.03340081 0.02125506
## 871 0.94534413 0.03340081 0.02125506
## 872 0.94534413 0.03340081 0.02125506
## 873 0.94534413 0.03340081 0.02125506
## 886 0.71111111 0.09135802 0.19753086
## 891 0.94534413 0.03340081 0.02125506
## 899 0.94534413 0.03340081 0.02125506
## 900 0.94534413 0.03340081 0.02125506
## 903 0.65656566 0.33333333 0.01010101
## 905 0.35238095 0.59047619 0.05714286
## 918 0.71111111 0.09135802 0.19753086
## 919 0.71111111 0.09135802 0.19753086
## 932 0.71111111 0.09135802 0.19753086
## 941 0.71111111 0.09135802 0.19753086
## 943 0.94534413 0.03340081 0.02125506
## 952 0.65656566 0.33333333 0.01010101
## 954 0.94534413 0.03340081 0.02125506
## 955 0.71111111 0.09135802 0.19753086
## 957 0.71111111 0.09135802 0.19753086
## 963 0.94534413 0.03340081 0.02125506
## 971 0.71111111 0.09135802 0.19753086
## 979 0.94534413 0.03340081 0.02125506
## 982 0.94534413 0.03340081 0.02125506
## 985 0.94534413 0.03340081 0.02125506
## 988 0.94534413 0.03340081 0.02125506
## 993 0.94534413 0.03340081 0.02125506
## 999 0.94534413 0.03340081 0.02125506
## 1001 0.94534413 0.03340081 0.02125506
## 1009 0.94534413 0.03340081 0.02125506
## 1010 0.94534413 0.03340081 0.02125506
## 1012 0.94534413 0.03340081 0.02125506
## 1020 0.94534413 0.03340081 0.02125506
## 1024 0.94534413 0.03340081 0.02125506
## 1027 0.94534413 0.03340081 0.02125506
## 1034 0.94534413 0.03340081 0.02125506
## 1039 0.94534413 0.03340081 0.02125506
## 1041 0.94534413 0.03340081 0.02125506
## 1045 0.71111111 0.09135802 0.19753086
## 1052 0.94534413 0.03340081 0.02125506
## 1058 0.71111111 0.09135802 0.19753086
## 1066 0.94534413 0.03340081 0.02125506
## 1067 0.94534413 0.03340081 0.02125506
## 1082 0.94534413 0.03340081 0.02125506
## 1083 0.94534413 0.03340081 0.02125506
## 1098 0.94534413 0.03340081 0.02125506
## 1114 0.71111111 0.09135802 0.19753086
## 1116 0.71111111 0.09135802 0.19753086
## 1117 0.71111111 0.09135802 0.19753086
## 1124 0.94534413 0.03340081 0.02125506
## 1131 0.94534413 0.03340081 0.02125506
## 1137 0.71111111 0.09135802 0.19753086
## 1140 0.71111111 0.09135802 0.19753086
## 1144 0.94534413 0.03340081 0.02125506
## 1162 0.94534413 0.03340081 0.02125506
## 1166 0.94534413 0.03340081 0.02125506
## 1175 0.94534413 0.03340081 0.02125506
## 1176 0.94534413 0.03340081 0.02125506
## 1184 0.71111111 0.09135802 0.19753086
## 1187 0.94534413 0.03340081 0.02125506
## 1206 0.65656566 0.33333333 0.01010101
## 1208 0.94534413 0.03340081 0.02125506
## 1213 0.94534413 0.03340081 0.02125506
## 1215 0.65656566 0.33333333 0.01010101
## 1216 0.94534413 0.03340081 0.02125506
## 1219 0.71111111 0.09135802 0.19753086
## 1222 0.71111111 0.09135802 0.19753086
## 1230 0.71111111 0.09135802 0.19753086
## 1233 0.94534413 0.03340081 0.02125506
## 1234 0.71111111 0.09135802 0.19753086
## 1236 0.94534413 0.03340081 0.02125506
## 1241 0.94534413 0.03340081 0.02125506
## 1251 0.94534413 0.03340081 0.02125506
## 1254 0.94534413 0.03340081 0.02125506
## 1257 0.94534413 0.03340081 0.02125506
## 1260 0.94534413 0.03340081 0.02125506
## 1261 0.71111111 0.09135802 0.19753086
## 1263 0.94534413 0.03340081 0.02125506
## 1264 0.94534413 0.03340081 0.02125506
## 1279 0.94534413 0.03340081 0.02125506
## 1280 0.94534413 0.03340081 0.02125506
## 1294 0.94534413 0.03340081 0.02125506
## 1296 0.94534413 0.03340081 0.02125506
## 1302 0.71111111 0.09135802 0.19753086
## 1306 0.65656566 0.33333333 0.01010101
## 1313 0.94534413 0.03340081 0.02125506
## 1318 0.71111111 0.09135802 0.19753086
## 1337 0.71111111 0.09135802 0.19753086
## 1341 0.94534413 0.03340081 0.02125506
## 1346 0.94534413 0.03340081 0.02125506
## 1352 0.71111111 0.09135802 0.19753086
## 1365 0.94534413 0.03340081 0.02125506
## 1372 0.65656566 0.33333333 0.01010101
## 1374 0.94534413 0.03340081 0.02125506
## 1376 0.94534413 0.03340081 0.02125506
## 1382 0.71111111 0.09135802 0.19753086
## 1384 0.71111111 0.09135802 0.19753086
## 1387 0.94534413 0.03340081 0.02125506
## 1393 0.65656566 0.33333333 0.01010101
## 1399 0.94534413 0.03340081 0.02125506
## 1403 0.35238095 0.59047619 0.05714286
## 1404 0.94534413 0.03340081 0.02125506
## 1406 0.35238095 0.59047619 0.05714286
## 1416 0.94534413 0.03340081 0.02125506
## 1421 0.94534413 0.03340081 0.02125506
## 1423 0.94534413 0.03340081 0.02125506
## 1432 0.65656566 0.33333333 0.01010101
## 1438 0.94534413 0.03340081 0.02125506
## 1439 0.94534413 0.03340081 0.02125506
## 1444 0.94534413 0.03340081 0.02125506
## 1445 0.94534413 0.03340081 0.02125506
## 1461 0.35238095 0.59047619 0.05714286
## 1462 0.35238095 0.59047619 0.05714286
## 1463 0.35238095 0.59047619 0.05714286
## 1464 0.35238095 0.59047619 0.05714286
## 1465 0.35238095 0.59047619 0.05714286
## 1473 0.94534413 0.03340081 0.02125506
## 1474 0.35238095 0.59047619 0.05714286
## 1482 0.94534413 0.03340081 0.02125506
## 1485 0.94534413 0.03340081 0.02125506
## 1490 0.71111111 0.09135802 0.19753086
## 1493 0.71111111 0.09135802 0.19753086
## 1499 0.94534413 0.03340081 0.02125506
## 1500 0.71111111 0.09135802 0.19753086
## 1504 0.94534413 0.03340081 0.02125506
## 1505 0.94534413 0.03340081 0.02125506
## 1508 0.71111111 0.09135802 0.19753086
## 1526 0.94534413 0.03340081 0.02125506
## 1527 0.71111111 0.09135802 0.19753086
## 1531 0.94534413 0.03340081 0.02125506
## 1539 0.71111111 0.09135802 0.19753086
## 1541 0.94534413 0.03340081 0.02125506
## 1547 0.35238095 0.59047619 0.05714286
## 1561 0.65656566 0.33333333 0.01010101
## 1582 0.94534413 0.03340081 0.02125506
## 1586 0.94534413 0.03340081 0.02125506
## 1593 0.71111111 0.09135802 0.19753086
## 1600 0.94534413 0.03340081 0.02125506
## 1607 0.94534413 0.03340081 0.02125506
## 1624 0.94534413 0.03340081 0.02125506
## 1629 0.94534413 0.03340081 0.02125506
## 1632 0.94534413 0.03340081 0.02125506
## 1636 0.94534413 0.03340081 0.02125506
## 1640 0.94534413 0.03340081 0.02125506
## 1643 0.94534413 0.03340081 0.02125506
## 1646 0.94534413 0.03340081 0.02125506
## 1648 0.94534413 0.03340081 0.02125506
## 1649 0.94534413 0.03340081 0.02125506
## 1651 0.94534413 0.03340081 0.02125506
## 1658 0.71111111 0.09135802 0.19753086
## 1660 0.94534413 0.03340081 0.02125506
## 1671 0.71111111 0.09135802 0.19753086
## 1673 0.94534413 0.03340081 0.02125506
## 1697 0.94534413 0.03340081 0.02125506
## 1707 0.94534413 0.03340081 0.02125506
## 1734 0.94534413 0.03340081 0.02125506
## 1739 0.94534413 0.03340081 0.02125506
## 1746 0.94534413 0.03340081 0.02125506
## 1755 0.94534413 0.03340081 0.02125506
## 1757 0.94534413 0.03340081 0.02125506
## 1760 0.94534413 0.03340081 0.02125506
## 1767 0.71111111 0.09135802 0.19753086
## 1769 0.71111111 0.09135802 0.19753086
## 1771 0.71111111 0.09135802 0.19753086
## 1777 0.71111111 0.09135802 0.19753086
## 1780 0.94534413 0.03340081 0.02125506
## 1783 0.94534413 0.03340081 0.02125506
## 1791 0.71111111 0.09135802 0.19753086
## 1797 0.94534413 0.03340081 0.02125506
## 1810 0.65656566 0.33333333 0.01010101
## 1811 0.65656566 0.33333333 0.01010101
## 1818 0.94534413 0.03340081 0.02125506
## 1820 0.94534413 0.03340081 0.02125506
## 1826 0.25842697 0.60674157 0.13483146
## 1833 0.25842697 0.60674157 0.13483146
## 1834 0.65656566 0.33333333 0.01010101
## 1860 0.94534413 0.03340081 0.02125506
## 1862 0.94534413 0.03340081 0.02125506
## 1863 0.94534413 0.03340081 0.02125506
## 1864 0.94534413 0.03340081 0.02125506
## 1875 0.94534413 0.03340081 0.02125506
## 1877 0.94534413 0.03340081 0.02125506
## 1881 0.65656566 0.33333333 0.01010101
## 1894 0.94534413 0.03340081 0.02125506
## 1900 0.94534413 0.03340081 0.02125506
## 1908 0.94534413 0.03340081 0.02125506
## 1910 0.94534413 0.03340081 0.02125506
## 1921 0.94534413 0.03340081 0.02125506
## 1922 0.94534413 0.03340081 0.02125506
## 1929 0.25842697 0.60674157 0.13483146
## 1930 0.25842697 0.60674157 0.13483146
## 1945 0.71111111 0.09135802 0.19753086
## 1951 0.71111111 0.09135802 0.19753086
## 1954 0.71111111 0.09135802 0.19753086
## 1961 0.94534413 0.03340081 0.02125506
## 1963 0.94534413 0.03340081 0.02125506
## 1964 0.71111111 0.09135802 0.19753086
## 1966 0.94534413 0.03340081 0.02125506
## 1972 0.94534413 0.03340081 0.02125506
## 1974 0.94534413 0.03340081 0.02125506
## 1976 0.94534413 0.03340081 0.02125506
## 1977 0.94534413 0.03340081 0.02125506
## 1980 0.94534413 0.03340081 0.02125506
## 1985 0.94534413 0.03340081 0.02125506
## 1987 0.94534413 0.03340081 0.02125506
## 1998 0.71111111 0.09135802 0.19753086
## 2012 0.71111111 0.09135802 0.19753086
## 2013 0.71111111 0.09135802 0.19753086
## 2021 0.71111111 0.09135802 0.19753086
## 2022 0.71111111 0.09135802 0.19753086
## 2023 0.71111111 0.09135802 0.19753086
## 2026 0.71111111 0.09135802 0.19753086
## 2044 0.71111111 0.09135802 0.19753086
## 2046 0.71111111 0.09135802 0.19753086
## 2053 0.94534413 0.03340081 0.02125506
## 2056 0.71111111 0.09135802 0.19753086
## 2061 0.71111111 0.09135802 0.19753086
## 2066 0.71111111 0.09135802 0.19753086
## 2073 0.94534413 0.03340081 0.02125506
## 2076 0.94534413 0.03340081 0.02125506
## 2089 0.71111111 0.09135802 0.19753086
## 2091 0.71111111 0.09135802 0.19753086
## 2098 0.71111111 0.09135802 0.19753086
## 2102 0.71111111 0.09135802 0.19753086
## 2104 0.71111111 0.09135802 0.19753086
## 2109 0.71111111 0.09135802 0.19753086
## 2116 0.65656566 0.33333333 0.01010101
## 2121 0.65656566 0.33333333 0.01010101
Evaluation of the model (training dataset)
The upper row - actual classification
The 1st column - prediction from model(tree)
from table, 70 - There are 70 patients who are actually suspect, but the model predict them as normal
row=column - number of perfect prediction
error_table<- table(predict(tree),train$NSPF)
print(error_table)##
## 1 2 3
## 1 1222 70 112
## 2 126 156 32
## 3 0 0 0
Misclassification Error (based on training dataset)
So the Misclassification Error of the model is about 19%
1-sum(diag(error_table))/sum(error_table)## [1] 0.1979045
Evaluation of the model (test dataset)
test_ev <- predict(tree,test)
error_table_test <- table(test_ev, test$NSPF)
print(error_table_test)##
## test_ev 1 2 3
## 1 274 21 28
## 2 33 48 4
## 3 0 0 0
Misclassification Error (based on test dataset)
1-sum(diag(error_table_test))/sum(error_table_test)## [1] 0.2107843