Cardiotocograph Analysis & Prediction
Mano R
31 July, 2020
Fetal cardiotocograms
Attribute Information:
LB - FHR baseline (beats per minute) AC - # of accelerations per second FM - # of fetal movements per second UC - # of uterine contractions per second DL - # of light decelerations per second DS - # of severe decelerations per second DP - # of prolongued decelerations per second ASTV - percentage of time with abnormal short term variability MSTV - mean value of short term variability ALTV - percentage of time with abnormal long term variability MLTV - mean value of long term variability Width - width of FHR histogram Min - minimum of FHR histogram Max - Maximum of FHR histogram Nmax - # of histogram peaks Nzeros - # of histogram zeros Mode - histogram mode Mean - histogram mean Median - histogram median Variance - histogram variance Tendency - histogram tendency CLASS - FHR pattern class code (1 to 10) NSP - fetal state class code (N=normal; S=suspect; P=pathologic)
In this dataset there are 2126 fetal cardiotocograms (CTGs) were automatically processed and the respective diagnostic features measured. The CTGs were also classified by three expert obstetricians and a consensus classification label assigned to each of them. Classification was both with respect to a morphologic pattern (A, B, C. …) and to a fetal state (N, S, P).
Acquring Dataset from uci
df <- read.csv('Cardiotocographic.csv')Structure of the dataset
str(df)## '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
df$NSPF <- factor(df$NSP)NSPF column
df$NSPF## [1] 2 1 1 1 1 3 3 3 3 3 2 2 1 1 1 1 1 2 1 1 3 1 3 3 3 3 3 3 2 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 3 3 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 3 1 1 1 1 1 1 1 2 2 2 2 2 1 1 1 1 1 1 2 1 2 2 2 1 1 2 1 1 1 1 1
## [149] 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 1 1
## [186] 1 1 2 2 1 2 2 2 2 2 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 2 2 2 2 1 1 1 1 2 2 2 1 1 1 1 1 1 1
## [260] 1 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 2 2 2 1 2 2 2 1 1 2 2
## [297] 3 3 3 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 3 2 3 3 2 2 3 3 2 2 2 3 2 2 2 2
## [334] 2 3 3 2 2 2 3 3 3 3 2 2 2 2 2 2 2 2 3 3 2 2 2 2 1 2 1 2 2 2 3 3 2 2 2 2 2
## [371] 2 1 2 2 2 2 2 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 2 2 2 2 1 2 3 2 2 2
## [408] 2 2 2 2 2 2 1 1 1 2 2 3 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 1 1 2 2
## [445] 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 3 3 3 3 3 3 3 3 3 3 2 2 2 1 2
## [482] 2 2 2 2 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 2 1 1 1 1
## [519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 2 2 2 1 2 1 1 2 2 2 2 1 2 2 1 1 1 1
## [556] 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 1 1
## [593] 1 1 1 2 3 3 2 2 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 1 1
## [630] 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 3 3 2 2 2 2 3 3 3 3 3 1 1 1 1 1 1 2 2 2 2 2
## [667] 2 2 2 1 1 1 2 1 1 1 1 1 1 1 1 1 3 3 1 1 1 1 1 2 3 1 1 1 1 1 1 3 3 2 3 3 3
## [704] 3 3 2 3 1 1 1 1 1 1 1 1 1 1 2 1 2 3 2 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1
## [741] 1 1 2 1 1 1 1 1 2 3 3 3 1 1 1 1 1 1 1 2 1 1 2 1 2 2 2 3 3 3 3 3 2 3 2 2 2
## [778] 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 2 1 2 2 1 1 1
## [815] 1 1 1 2 1 2 2 2 2 3 3 2 1 1 2 1 1 2 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1
## [852] 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 1 1
## [889] 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 1 1
## [926] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [963] 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 1 1
## [1000] 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 1 1
## [1037] 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 1 1
## [1074] 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 1 1
## [1111] 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 1 1
## [1148] 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 1 1
## [1185] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 2 2 2 2 1 1 1 1 1
## [1222] 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 1 1
## [1259] 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 1 1
## [1296] 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 1 1
## [1333] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1370] 1 2 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 2 1 2 2 1 1 1 1 1 1 2 2 2 2
## [1407] 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1
## [1444] 1 1 2 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 2 1 2 1 1 1 1 1 2 1 1 2 2 2 2 1 1 1
## [1481] 1 1 1 1 1 1 1 1 1 3 2 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1518] 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 2 2 2 2 1 1 1 1
## [1555] 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 1 1
## [1592] 1 1 1 1 1 1 1 1 1 2 1 2 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1629] 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 1 1
## [1666] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1703] 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1
## [1740] 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3 1 1 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1777] 3 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1814] 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 1 1
## [1851] 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 3 3 3 3 1 1 1 1 1
## [1888] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1925] 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1962] 3 3 3 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
## [1999] 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 3 3 3 3 3 3 3 3 3 3
## [2036] 3 3 3 3 3 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2073] 1 1 1 1 1 1 1 1 1 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
## [2110] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1
## Levels: 1 2 3Exploratort data Analysis
library(ggplot2)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutpl <- ggplot(df,aes(LB,FM,color=NSP))+geom_line(aes(frame= LB))+ggtitle("Featal heartbeat Ratio")+xlab("Time/min")+ylab("Fetal movements/sec")## Warning: Ignoring unknown aesthetics: framefig <- ggplotly(pl)
figpl <- ggplot(df,aes(LB,UC,color=factor(Nzeros)))+geom_point(aes(frame= LB))+ggtitle("Featal heartbeat Ratio")+xlab("Time/min")+ylab("Uterine contractions")## Warning: Ignoring unknown aesthetics: framefig <- ggplotly(pl)
fig## Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
## replace is not a multiple of replacement length
Data Partitioning
Split into train and test data
library(caTools)
sample <- sample.split(df$NSPF,SplitRatio = 0.80)
train <- subset(df,sample == T)
test <- subset(df,sample == F)library(party)## Loading required package: grid## Loading required package: mvtnorm## Loading required package: modeltools## Loading required package: stats4## Loading required package: strucchange## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Loading required package: sandwichDecision_tree <- ctree(NSPF~LB+AC+FM, data = train)
Decision_tree##
## Conditional inference tree with 17 terminal nodes
##
## Response: NSPF
## Inputs: LB, AC, FM
## Number of observations: 1701
##
## 1) AC <= 0.000823723; criterion = 1, statistic = 260.323
## 2) LB <= 136; criterion = 1, statistic = 126.15
## 3) FM <= 0.001545595; criterion = 1, statistic = 22.23
## 4) FM <= 0.000729927; criterion = 0.968, statistic = 9.066
## 5)* weights = 244
## 4) FM > 0.000729927
## 6)* weights = 49
## 3) FM > 0.001545595
## 7) FM <= 0.1351126; criterion = 1, statistic = 29.687
## 8)* weights = 112
## 7) FM > 0.1351126
## 9)* weights = 7
## 2) LB > 136
## 10) LB <= 143; criterion = 0.989, statistic = 11.153
## 11)* weights = 138
## 10) LB > 143
## 12) FM <= 0.001312336; criterion = 0.998, statistic = 15.106
## 13)* weights = 126
## 12) FM > 0.001312336
## 14) LB <= 146; criterion = 0.999, statistic = 15.673
## 15)* weights = 29
## 14) LB > 146
## 16)* weights = 17
## 1) AC > 0.000823723
## 17) AC <= 0.002053388; criterion = 1, statistic = 54.714
## 18) LB <= 128; criterion = 0.999, statistic = 15.808
## 19)* weights = 46
## 18) LB > 128
## 20) LB <= 134; criterion = 1, statistic = 18.641
## 21) FM <= 0.001953125; criterion = 1, statistic = 18.633
## 22)* weights = 35
## 21) FM > 0.001953125
## 23)* weights = 7
## 20) LB > 134
## 24)* weights = 97
## 17) AC > 0.002053388
## 25) AC <= 0.002923977; criterion = 0.999, statistic = 16.57
## 26) LB <= 144; criterion = 0.992, statistic = 8.917
## 27)* weights = 108
## 26) LB > 144
## 28)* weights = 8
## 25) AC > 0.002923977
## 29) FM <= 0.3802228; criterion = 1, statistic = 24.396
## 30) AC <= 0.004444444; criterion = 0.994, statistic = 12.362
## 31)* weights = 160
## 30) AC > 0.004444444
## 32)* weights = 508
## 29) FM > 0.3802228
## 33)* weights = 10plot(Decision_tree)
Decision_tree <- ctree(NSPF~LB+AC+FM, data = train, controls = ctree_control(mincriterion = 0.999,minsplit = 650))
Decision_tree##
## Conditional inference tree with 6 terminal nodes
##
## Response: NSPF
## Inputs: LB, AC, FM
## Number of observations: 1701
##
## 1) AC <= 0.000823723; criterion = 1, statistic = 260.323
## 2) LB <= 136; criterion = 1, statistic = 126.15
## 3)* weights = 412
## 2) LB > 136
## 4)* weights = 310
## 1) AC > 0.000823723
## 5) AC <= 0.002053388; criterion = 1, statistic = 54.714
## 6)* weights = 185
## 5) AC > 0.002053388
## 7) AC <= 0.002923977; criterion = 0.999, statistic = 16.57
## 8)* weights = 116
## 7) AC > 0.002923977
## 9) FM <= 0.3802228; criterion = 1, statistic = 24.396
## 10)* weights = 668
## 9) FM > 0.3802228
## 11)* weights = 10plot(Decision_tree)
predicted_tree <- Predict(Decision_tree,test,type="prob")predicted_tree## [[1]]
## [1] 0.982035928 0.001497006 0.016467066
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## [1] 0.6796117 0.1067961 0.2135922
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## [1] 0.982035928 0.001497006 0.016467066
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## [1] 0.78918919 0.14054054 0.07027027
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## [1] 0.982035928 0.001497006 0.016467066
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## [1] 0.982035928 0.001497006 0.016467066
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## [1] 0.40000000 0.50645161 0.09354839
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## [1] 0.40000000 0.50645161 0.09354839
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## [1] 0.982035928 0.001497006 0.016467066
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## [1] 0.40000000 0.50645161 0.09354839
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## [1] 0.982035928 0.001497006 0.016467066
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##
## [[370]]
## [1] 0.40000000 0.50645161 0.09354839
##
## [[371]]
## [1] 0.40000000 0.50645161 0.09354839
##
## [[372]]
## [1] 0.40000000 0.50645161 0.09354839
##
## [[373]]
## [1] 0.40000000 0.50645161 0.09354839
##
## [[374]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[375]]
## [1] 0.40000000 0.50645161 0.09354839
##
## [[376]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[377]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[378]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[379]]
## [1] 0.93965517 0.06034483 0.00000000
##
## [[380]]
## [1] 0.93965517 0.06034483 0.00000000
##
## [[381]]
## [1] 0.40000000 0.50645161 0.09354839
##
## [[382]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[383]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[384]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[385]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[386]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[387]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[388]]
## [1] 0.78918919 0.14054054 0.07027027
##
## [[389]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[390]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[391]]
## [1] 0.78918919 0.14054054 0.07027027
##
## [[392]]
## [1] 0.78918919 0.14054054 0.07027027
##
## [[393]]
## [1] 0.78918919 0.14054054 0.07027027
##
## [[394]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[395]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[396]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[397]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[398]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[399]]
## [1] 0.78918919 0.14054054 0.07027027
##
## [[400]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[401]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[402]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[403]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[404]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[405]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[406]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[407]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[408]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[409]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[410]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[411]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[412]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[413]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[414]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[415]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[416]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[417]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[418]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[419]]
## [1] 0.982035928 0.001497006 0.016467066
##
## [[420]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[421]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[422]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[423]]
## [1] 0.6796117 0.1067961 0.2135922
##
## [[424]]
## [1] 0.40000000 0.50645161 0.09354839
##
## [[425]]
## [1] 0.78918919 0.14054054 0.07027027Prediction <- predict(Decision_tree,test)
Prediction## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 2 1 1 1 1 2 1 2 2 1 1 1 1 1 1 1 1 2 1 2 1
## [38] 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 1 2 2 2 2 2 2
## [75] 2 2 2 2 1 1 2 2 1 1 1 1 2 2 2 1 1 2 2 2 1 1 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1
## [112] 1 1 2 2 2 2 1 2 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 2 1 1 1 1 2 2 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 2 1 1 1 1 1 2 2 1 2 1 2 1 2 1 1 2 2 1 1 2 1 1 1 1 1 1 1 1 1
## [186] 1 1 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 1 1 1 1
## [223] 1 1 1 1 1 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 1 1 1 1 1
## [260] 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 1 1
## [297] 2 2 2 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 2
## [334] 2 2 2 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 1 1 1 1 2 2 2
## [371] 2 2 2 1 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 1
## [408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1
## Levels: 1 2 3library(rpart)
model_tree <- rpart(NSPF~LB+AC+FM, train)
model_tree## n= 1701
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1701 377 1 (0.77836567 0.13874192 0.08289242)
## 2) AC>=0.0008261115 979 59 1 (0.93973442 0.03575077 0.02451481) *
## 3) AC< 0.0008261115 722 318 1 (0.55955679 0.27839335 0.16204986)
## 6) LB< 136.5 412 132 1 (0.67961165 0.10679612 0.21359223)
## 12) FM< 0.2165359 405 125 1 (0.69135802 0.10864198 0.20000000)
## 24) FM>=0.004432953 69 28 1 (0.59420290 0.30434783 0.10144928) *
## 25) FM< 0.004432953 336 97 1 (0.71130952 0.06845238 0.22023810)
## 50) LB>=121.5 286 73 1 (0.74475524 0.06993007 0.18531469) *
## 51) LB< 121.5 50 24 1 (0.52000000 0.06000000 0.42000000)
## 102) LB< 119.5 19 0 1 (1.00000000 0.00000000 0.00000000) *
## 103) LB>=119.5 31 10 3 (0.22580645 0.09677419 0.67741935) *
## 13) FM>=0.2165359 7 0 3 (0.00000000 0.00000000 1.00000000) *
## 7) LB>=136.5 310 153 2 (0.40000000 0.50645161 0.09354839)
## 14) LB< 143.5 138 61 1 (0.55797101 0.39130435 0.05072464)
## 28) FM< 0.003750276 107 40 1 (0.62616822 0.32710280 0.04672897) *
## 29) FM>=0.003750276 31 12 2 (0.32258065 0.61290323 0.06451613) *
## 15) LB>=143.5 172 69 2 (0.27325581 0.59883721 0.12790698)
## 30) FM< 0.001300498 125 51 2 (0.36000000 0.59200000 0.04800000) *
## 31) FM>=0.001300498 47 18 2 (0.04255319 0.61702128 0.34042553)
## 62) LB< 147 29 5 2 (0.06896552 0.82758621 0.10344828) *
## 63) LB>=147 18 5 3 (0.00000000 0.27777778 0.72222222) *library(rpart.plot)
rpart.plot(model_tree)
rpart.plot(model_tree,extra=2)
Final_prediction<-predict(model_tree,test)
Final_prediction## 1 2 3
## 5 0.93973442 0.03575077 0.02451481
## 10 0.74475524 0.06993007 0.18531469
## 13 0.93973442 0.03575077 0.02451481
## 21 0.00000000 0.00000000 1.00000000
## 24 0.74475524 0.06993007 0.18531469
## 25 0.74475524 0.06993007 0.18531469
## 27 0.74475524 0.06993007 0.18531469
## 28 0.74475524 0.06993007 0.18531469
## 31 0.59420290 0.30434783 0.10144928
## 39 0.93973442 0.03575077 0.02451481
## 43 0.93973442 0.03575077 0.02451481
## 44 0.93973442 0.03575077 0.02451481
## 49 0.93973442 0.03575077 0.02451481
## 52 0.36000000 0.59200000 0.04800000
## 58 0.36000000 0.59200000 0.04800000
## 60 0.93973442 0.03575077 0.02451481
## 70 0.36000000 0.59200000 0.04800000
## 72 0.93973442 0.03575077 0.02451481
## 73 0.93973442 0.03575077 0.02451481
## 78 0.93973442 0.03575077 0.02451481
## 79 0.93973442 0.03575077 0.02451481
## 83 0.06896552 0.82758621 0.10344828
## 91 0.93973442 0.03575077 0.02451481
## 93 0.00000000 0.27777778 0.72222222
## 95 0.00000000 0.27777778 0.72222222
## 102 0.93973442 0.03575077 0.02451481
## 103 0.93973442 0.03575077 0.02451481
## 107 0.59420290 0.30434783 0.10144928
## 109 0.93973442 0.03575077 0.02451481
## 110 0.74475524 0.06993007 0.18531469
## 112 0.93973442 0.03575077 0.02451481
## 117 0.74475524 0.06993007 0.18531469
## 121 0.74475524 0.06993007 0.18531469
## 127 0.36000000 0.59200000 0.04800000
## 139 0.93973442 0.03575077 0.02451481
## 143 0.36000000 0.59200000 0.04800000
## 154 0.93973442 0.03575077 0.02451481
## 155 0.74475524 0.06993007 0.18531469
## 160 0.93973442 0.03575077 0.02451481
## 163 0.93973442 0.03575077 0.02451481
## 176 0.93973442 0.03575077 0.02451481
## 177 1.00000000 0.00000000 0.00000000
## 182 0.93973442 0.03575077 0.02451481
## 190 0.93973442 0.03575077 0.02451481
## 193 0.93973442 0.03575077 0.02451481
## 196 0.36000000 0.59200000 0.04800000
## 201 0.59420290 0.30434783 0.10144928
## 202 0.59420290 0.30434783 0.10144928
## 214 0.93973442 0.03575077 0.02451481
## 215 0.93973442 0.03575077 0.02451481
## 226 0.59420290 0.30434783 0.10144928
## 229 0.93973442 0.03575077 0.02451481
## 235 0.74475524 0.06993007 0.18531469
## 237 0.93973442 0.03575077 0.02451481
## 240 0.59420290 0.30434783 0.10144928
## 249 0.93973442 0.03575077 0.02451481
## 257 0.93973442 0.03575077 0.02451481
## 259 0.93973442 0.03575077 0.02451481
## 261 0.74475524 0.06993007 0.18531469
## 265 0.74475524 0.06993007 0.18531469
## 280 0.93973442 0.03575077 0.02451481
## 295 0.06896552 0.82758621 0.10344828
## 302 0.06896552 0.82758621 0.10344828
## 303 0.06896552 0.82758621 0.10344828
## 313 0.06896552 0.82758621 0.10344828
## 316 0.06896552 0.82758621 0.10344828
## 324 0.74475524 0.06993007 0.18531469
## 325 0.74475524 0.06993007 0.18531469
## 331 0.06896552 0.82758621 0.10344828
## 332 0.06896552 0.82758621 0.10344828
## 335 0.06896552 0.82758621 0.10344828
## 336 0.06896552 0.82758621 0.10344828
## 337 0.32258065 0.61290323 0.06451613
## 339 0.32258065 0.61290323 0.06451613
## 344 0.32258065 0.61290323 0.06451613
## 349 0.32258065 0.61290323 0.06451613
## 352 0.32258065 0.61290323 0.06451613
## 355 0.32258065 0.61290323 0.06451613
## 362 0.59420290 0.30434783 0.10144928
## 366 0.59420290 0.30434783 0.10144928
## 370 0.32258065 0.61290323 0.06451613
## 371 0.62616822 0.32710280 0.04672897
## 379 0.93973442 0.03575077 0.02451481
## 383 0.93973442 0.03575077 0.02451481
## 384 0.93973442 0.03575077 0.02451481
## 387 0.93973442 0.03575077 0.02451481
## 396 0.62616822 0.32710280 0.04672897
## 399 0.36000000 0.59200000 0.04800000
## 406 0.36000000 0.59200000 0.04800000
## 415 0.93973442 0.03575077 0.02451481
## 421 0.93973442 0.03575077 0.02451481
## 422 0.62616822 0.32710280 0.04672897
## 425 0.62616822 0.32710280 0.04672897
## 426 0.00000000 0.27777778 0.72222222
## 431 0.93973442 0.03575077 0.02451481
## 433 0.93973442 0.03575077 0.02451481
## 439 0.36000000 0.59200000 0.04800000
## 440 0.36000000 0.59200000 0.04800000
## 447 0.32258065 0.61290323 0.06451613
## 471 0.00000000 0.27777778 0.72222222
## 474 0.00000000 0.27777778 0.72222222
## 477 0.36000000 0.59200000 0.04800000
## 486 0.62616822 0.32710280 0.04672897
## 492 0.93973442 0.03575077 0.02451481
## 493 0.93973442 0.03575077 0.02451481
## 494 0.93973442 0.03575077 0.02451481
## 496 0.93973442 0.03575077 0.02451481
## 497 0.93973442 0.03575077 0.02451481
## 501 0.93973442 0.03575077 0.02451481
## 503 0.93973442 0.03575077 0.02451481
## 513 0.93973442 0.03575077 0.02451481
## 531 0.93973442 0.03575077 0.02451481
## 537 0.93973442 0.03575077 0.02451481
## 538 0.62616822 0.32710280 0.04672897
## 540 0.62616822 0.32710280 0.04672897
## 549 0.62616822 0.32710280 0.04672897
## 551 0.62616822 0.32710280 0.04672897
## 553 0.93973442 0.03575077 0.02451481
## 555 0.62616822 0.32710280 0.04672897
## 556 0.93973442 0.03575077 0.02451481
## 557 0.93973442 0.03575077 0.02451481
## 568 0.93973442 0.03575077 0.02451481
## 569 0.93973442 0.03575077 0.02451481
## 570 0.93973442 0.03575077 0.02451481
## 572 0.93973442 0.03575077 0.02451481
## 573 0.93973442 0.03575077 0.02451481
## 596 0.00000000 0.27777778 0.72222222
## 599 0.00000000 0.27777778 0.72222222
## 606 0.93973442 0.03575077 0.02451481
## 608 0.93973442 0.03575077 0.02451481
## 610 0.93973442 0.03575077 0.02451481
## 628 0.93973442 0.03575077 0.02451481
## 629 0.22580645 0.09677419 0.67741935
## 631 0.93973442 0.03575077 0.02451481
## 636 0.62616822 0.32710280 0.04672897
## 645 0.74475524 0.06993007 0.18531469
## 648 0.74475524 0.06993007 0.18531469
## 649 0.74475524 0.06993007 0.18531469
## 659 0.93973442 0.03575077 0.02451481
## 665 0.62616822 0.32710280 0.04672897
## 667 0.32258065 0.61290323 0.06451613
## 689 0.93973442 0.03575077 0.02451481
## 696 0.93973442 0.03575077 0.02451481
## 702 0.00000000 0.00000000 1.00000000
## 703 0.00000000 0.00000000 1.00000000
## 705 0.00000000 0.00000000 1.00000000
## 707 0.74475524 0.06993007 0.18531469
## 710 0.74475524 0.06993007 0.18531469
## 716 0.93973442 0.03575077 0.02451481
## 717 0.93973442 0.03575077 0.02451481
## 721 0.59420290 0.30434783 0.10144928
## 725 0.93973442 0.03575077 0.02451481
## 726 0.93973442 0.03575077 0.02451481
## 734 0.93973442 0.03575077 0.02451481
## 739 0.93973442 0.03575077 0.02451481
## 745 0.62616822 0.32710280 0.04672897
## 752 0.74475524 0.06993007 0.18531469
## 762 0.93973442 0.03575077 0.02451481
## 780 0.74475524 0.06993007 0.18531469
## 786 0.93973442 0.03575077 0.02451481
## 791 0.93973442 0.03575077 0.02451481
## 793 0.36000000 0.59200000 0.04800000
## 794 0.36000000 0.59200000 0.04800000
## 809 0.93973442 0.03575077 0.02451481
## 811 0.36000000 0.59200000 0.04800000
## 812 0.93973442 0.03575077 0.02451481
## 816 0.36000000 0.59200000 0.04800000
## 817 0.93973442 0.03575077 0.02451481
## 827 0.36000000 0.59200000 0.04800000
## 836 0.93973442 0.03575077 0.02451481
## 838 0.93973442 0.03575077 0.02451481
## 840 0.62616822 0.32710280 0.04672897
## 843 0.62616822 0.32710280 0.04672897
## 849 0.93973442 0.03575077 0.02451481
## 850 0.93973442 0.03575077 0.02451481
## 854 0.62616822 0.32710280 0.04672897
## 871 0.93973442 0.03575077 0.02451481
## 876 0.93973442 0.03575077 0.02451481
## 880 0.93973442 0.03575077 0.02451481
## 884 0.93973442 0.03575077 0.02451481
## 885 0.93973442 0.03575077 0.02451481
## 887 0.93973442 0.03575077 0.02451481
## 890 0.93973442 0.03575077 0.02451481
## 892 0.74475524 0.06993007 0.18531469
## 893 0.93973442 0.03575077 0.02451481
## 900 0.93973442 0.03575077 0.02451481
## 908 0.93973442 0.03575077 0.02451481
## 910 0.93973442 0.03575077 0.02451481
## 919 0.74475524 0.06993007 0.18531469
## 927 0.74475524 0.06993007 0.18531469
## 931 0.93973442 0.03575077 0.02451481
## 940 0.74475524 0.06993007 0.18531469
## 947 0.62616822 0.32710280 0.04672897
## 948 0.93973442 0.03575077 0.02451481
## 954 0.93973442 0.03575077 0.02451481
## 956 0.74475524 0.06993007 0.18531469
## 957 0.74475524 0.06993007 0.18531469
## 960 0.74475524 0.06993007 0.18531469
## 982 0.93973442 0.03575077 0.02451481
## 983 0.74475524 0.06993007 0.18531469
## 984 0.74475524 0.06993007 0.18531469
## 985 0.93973442 0.03575077 0.02451481
## 986 0.93973442 0.03575077 0.02451481
## 996 0.93973442 0.03575077 0.02451481
## 1009 0.93973442 0.03575077 0.02451481
## 1010 0.93973442 0.03575077 0.02451481
## 1014 0.93973442 0.03575077 0.02451481
## 1015 0.93973442 0.03575077 0.02451481
## 1016 0.93973442 0.03575077 0.02451481
## 1019 0.93973442 0.03575077 0.02451481
## 1021 0.93973442 0.03575077 0.02451481
## 1023 0.93973442 0.03575077 0.02451481
## 1041 0.93973442 0.03575077 0.02451481
## 1043 0.74475524 0.06993007 0.18531469
## 1044 0.93973442 0.03575077 0.02451481
## 1045 0.74475524 0.06993007 0.18531469
## 1047 0.93973442 0.03575077 0.02451481
## 1052 0.93973442 0.03575077 0.02451481
## 1056 0.93973442 0.03575077 0.02451481
## 1057 0.93973442 0.03575077 0.02451481
## 1059 0.74475524 0.06993007 0.18531469
## 1062 0.93973442 0.03575077 0.02451481
## 1067 0.93973442 0.03575077 0.02451481
## 1070 0.93973442 0.03575077 0.02451481
## 1072 0.93973442 0.03575077 0.02451481
## 1075 0.93973442 0.03575077 0.02451481
## 1076 0.93973442 0.03575077 0.02451481
## 1079 0.74475524 0.06993007 0.18531469
## 1082 0.93973442 0.03575077 0.02451481
## 1085 0.93973442 0.03575077 0.02451481
## 1092 0.93973442 0.03575077 0.02451481
## 1094 0.93973442 0.03575077 0.02451481
## 1111 0.93973442 0.03575077 0.02451481
## 1115 0.74475524 0.06993007 0.18531469
## 1132 0.93973442 0.03575077 0.02451481
## 1135 0.93973442 0.03575077 0.02451481
## 1136 0.93973442 0.03575077 0.02451481
## 1137 0.74475524 0.06993007 0.18531469
## 1147 0.74475524 0.06993007 0.18531469
## 1149 0.74475524 0.06993007 0.18531469
## 1160 0.93973442 0.03575077 0.02451481
## 1169 0.74475524 0.06993007 0.18531469
## 1181 0.93973442 0.03575077 0.02451481
## 1182 0.93973442 0.03575077 0.02451481
## 1195 0.93973442 0.03575077 0.02451481
## 1206 0.62616822 0.32710280 0.04672897
## 1208 0.93973442 0.03575077 0.02451481
## 1213 0.93973442 0.03575077 0.02451481
## 1216 0.93973442 0.03575077 0.02451481
## 1217 0.74475524 0.06993007 0.18531469
## 1218 0.74475524 0.06993007 0.18531469
## 1226 0.93973442 0.03575077 0.02451481
## 1227 0.93973442 0.03575077 0.02451481
## 1233 0.93973442 0.03575077 0.02451481
## 1239 0.93973442 0.03575077 0.02451481
## 1244 0.93973442 0.03575077 0.02451481
## 1251 0.93973442 0.03575077 0.02451481
## 1253 1.00000000 0.00000000 0.00000000
## 1257 0.93973442 0.03575077 0.02451481
## 1261 1.00000000 0.00000000 0.00000000
## 1262 1.00000000 0.00000000 0.00000000
## 1267 0.93973442 0.03575077 0.02451481
## 1268 1.00000000 0.00000000 0.00000000
## 1271 1.00000000 0.00000000 0.00000000
## 1272 1.00000000 0.00000000 0.00000000
## 1273 0.93973442 0.03575077 0.02451481
## 1276 0.93973442 0.03575077 0.02451481
## 1286 1.00000000 0.00000000 0.00000000
## 1293 0.93973442 0.03575077 0.02451481
## 1294 0.93973442 0.03575077 0.02451481
## 1297 0.93973442 0.03575077 0.02451481
## 1299 0.93973442 0.03575077 0.02451481
## 1300 0.93973442 0.03575077 0.02451481
## 1303 0.93973442 0.03575077 0.02451481
## 1317 0.93973442 0.03575077 0.02451481
## 1323 0.59420290 0.30434783 0.10144928
## 1324 0.93973442 0.03575077 0.02451481
## 1331 0.93973442 0.03575077 0.02451481
## 1333 0.93973442 0.03575077 0.02451481
## 1334 0.93973442 0.03575077 0.02451481
## 1337 0.59420290 0.30434783 0.10144928
## 1338 0.93973442 0.03575077 0.02451481
## 1341 0.93973442 0.03575077 0.02451481
## 1353 0.93973442 0.03575077 0.02451481
## 1356 0.93973442 0.03575077 0.02451481
## 1358 0.93973442 0.03575077 0.02451481
## 1360 0.93973442 0.03575077 0.02451481
## 1365 0.93973442 0.03575077 0.02451481
## 1366 0.74475524 0.06993007 0.18531469
## 1377 0.93973442 0.03575077 0.02451481
## 1378 0.93973442 0.03575077 0.02451481
## 1382 0.22580645 0.09677419 0.67741935
## 1383 0.93973442 0.03575077 0.02451481
## 1384 0.22580645 0.09677419 0.67741935
## 1386 0.22580645 0.09677419 0.67741935
## 1389 0.93973442 0.03575077 0.02451481
## 1393 0.62616822 0.32710280 0.04672897
## 1394 0.62616822 0.32710280 0.04672897
## 1395 0.62616822 0.32710280 0.04672897
## 1397 0.93973442 0.03575077 0.02451481
## 1415 0.93973442 0.03575077 0.02451481
## 1416 0.93973442 0.03575077 0.02451481
## 1419 0.93973442 0.03575077 0.02451481
## 1426 0.93973442 0.03575077 0.02451481
## 1431 0.93973442 0.03575077 0.02451481
## 1432 0.36000000 0.59200000 0.04800000
## 1449 0.93973442 0.03575077 0.02451481
## 1450 0.93973442 0.03575077 0.02451481
## 1460 0.93973442 0.03575077 0.02451481
## 1468 0.93973442 0.03575077 0.02451481
## 1471 0.36000000 0.59200000 0.04800000
## 1482 0.93973442 0.03575077 0.02451481
## 1483 0.74475524 0.06993007 0.18531469
## 1488 0.93973442 0.03575077 0.02451481
## 1494 0.74475524 0.06993007 0.18531469
## 1495 0.93973442 0.03575077 0.02451481
## 1501 0.93973442 0.03575077 0.02451481
## 1507 0.93973442 0.03575077 0.02451481
## 1512 0.93973442 0.03575077 0.02451481
## 1519 0.74475524 0.06993007 0.18531469
## 1535 0.74475524 0.06993007 0.18531469
## 1540 0.93973442 0.03575077 0.02451481
## 1550 0.36000000 0.59200000 0.04800000
## 1555 0.93973442 0.03575077 0.02451481
## 1557 0.93973442 0.03575077 0.02451481
## 1571 0.93973442 0.03575077 0.02451481
## 1579 0.74475524 0.06993007 0.18531469
## 1582 0.93973442 0.03575077 0.02451481
## 1587 0.74475524 0.06993007 0.18531469
## 1590 0.74475524 0.06993007 0.18531469
## 1598 0.62616822 0.32710280 0.04672897
## 1600 0.93973442 0.03575077 0.02451481
## 1601 0.62616822 0.32710280 0.04672897
## 1605 0.62616822 0.32710280 0.04672897
## 1606 0.62616822 0.32710280 0.04672897
## 1610 0.06896552 0.82758621 0.10344828
## 1612 0.93973442 0.03575077 0.02451481
## 1616 0.06896552 0.82758621 0.10344828
## 1617 0.93973442 0.03575077 0.02451481
## 1626 0.93973442 0.03575077 0.02451481
## 1630 0.93973442 0.03575077 0.02451481
## 1642 0.93973442 0.03575077 0.02451481
## 1645 0.93973442 0.03575077 0.02451481
## 1647 0.93973442 0.03575077 0.02451481
## 1649 0.93973442 0.03575077 0.02451481
## 1651 0.93973442 0.03575077 0.02451481
## 1657 1.00000000 0.00000000 0.00000000
## 1665 1.00000000 0.00000000 0.00000000
## 1670 0.93973442 0.03575077 0.02451481
## 1672 0.93973442 0.03575077 0.02451481
## 1673 0.93973442 0.03575077 0.02451481
## 1685 0.93973442 0.03575077 0.02451481
## 1687 0.93973442 0.03575077 0.02451481
## 1688 0.93973442 0.03575077 0.02451481
## 1692 0.93973442 0.03575077 0.02451481
## 1699 0.93973442 0.03575077 0.02451481
## 1704 0.93973442 0.03575077 0.02451481
## 1706 0.93973442 0.03575077 0.02451481
## 1714 0.93973442 0.03575077 0.02451481
## 1725 0.93973442 0.03575077 0.02451481
## 1727 0.93973442 0.03575077 0.02451481
## 1739 0.93973442 0.03575077 0.02451481
## 1745 0.93973442 0.03575077 0.02451481
## 1747 0.93973442 0.03575077 0.02451481
## 1768 0.22580645 0.09677419 0.67741935
## 1787 0.93973442 0.03575077 0.02451481
## 1798 0.93973442 0.03575077 0.02451481
## 1800 0.62616822 0.32710280 0.04672897
## 1802 0.62616822 0.32710280 0.04672897
## 1803 0.62616822 0.32710280 0.04672897
## 1805 0.62616822 0.32710280 0.04672897
## 1807 0.62616822 0.32710280 0.04672897
## 1811 0.62616822 0.32710280 0.04672897
## 1817 0.93973442 0.03575077 0.02451481
## 1844 0.62616822 0.32710280 0.04672897
## 1850 0.93973442 0.03575077 0.02451481
## 1863 0.93973442 0.03575077 0.02451481
## 1867 0.93973442 0.03575077 0.02451481
## 1873 0.93973442 0.03575077 0.02451481
## 1874 0.93973442 0.03575077 0.02451481
## 1882 0.62616822 0.32710280 0.04672897
## 1885 0.93973442 0.03575077 0.02451481
## 1887 0.93973442 0.03575077 0.02451481
## 1890 0.93973442 0.03575077 0.02451481
## 1893 0.93973442 0.03575077 0.02451481
## 1895 0.93973442 0.03575077 0.02451481
## 1898 0.93973442 0.03575077 0.02451481
## 1910 0.93973442 0.03575077 0.02451481
## 1914 0.93973442 0.03575077 0.02451481
## 1915 0.93973442 0.03575077 0.02451481
## 1919 0.93973442 0.03575077 0.02451481
## 1923 0.93973442 0.03575077 0.02451481
## 1925 0.93973442 0.03575077 0.02451481
## 1939 0.74475524 0.06993007 0.18531469
## 1946 0.74475524 0.06993007 0.18531469
## 1948 0.74475524 0.06993007 0.18531469
## 1956 0.74475524 0.06993007 0.18531469
## 1957 0.74475524 0.06993007 0.18531469
## 1963 0.93973442 0.03575077 0.02451481
## 1964 0.74475524 0.06993007 0.18531469
## 1977 0.93973442 0.03575077 0.02451481
## 1983 0.93973442 0.03575077 0.02451481
## 1989 0.93973442 0.03575077 0.02451481
## 1992 0.93973442 0.03575077 0.02451481
## 1998 0.74475524 0.06993007 0.18531469
## 2003 0.74475524 0.06993007 0.18531469
## 2010 0.74475524 0.06993007 0.18531469
## 2015 0.74475524 0.06993007 0.18531469
## 2017 0.74475524 0.06993007 0.18531469
## 2025 0.74475524 0.06993007 0.18531469
## 2033 0.74475524 0.06993007 0.18531469
## 2049 0.74475524 0.06993007 0.18531469
## 2050 0.93973442 0.03575077 0.02451481
## 2055 0.74475524 0.06993007 0.18531469
## 2062 0.74475524 0.06993007 0.18531469
## 2064 0.74475524 0.06993007 0.18531469
## 2067 0.93973442 0.03575077 0.02451481
## 2068 0.93973442 0.03575077 0.02451481
## 2081 0.93973442 0.03575077 0.02451481
## 2083 0.59420290 0.30434783 0.10144928
## 2098 0.74475524 0.06993007 0.18531469
## 2106 0.74475524 0.06993007 0.18531469
## 2112 0.74475524 0.06993007 0.18531469
## 2115 0.62616822 0.32710280 0.04672897
## 2124 0.93973442 0.03575077 0.02451481
Evaluation of the model
Cm <- predict(model_tree,test)
Confusionmatrix <- table(Prediction, test$NSPF)
print(Confusionmatrix)##
## Prediction 1 2 3
## 1 302 14 28
## 2 29 45 7
## 3 0 0 0