Leaf Classication with XGBoost
Aditya S Nakate
6 January 2017
Following is the R code for ‘Leaf Classification’ problem on Kaggle. I have used XGBoost technique to solve this problem and Cross Validation method to find out the ideal parameters. As the dataset had around 190 attributes per observation Principal Component Analysis was used for dimensionality reduction for better performance.
# load required packages
library(readr)
library(ggplot2)
library(caret)
library(Matrix)
library(plyr)
library(xgboost)
library(car)
library(doBy)
# read training and testing datasets
train <- read.csv("C:/Users/Adi/Desktop/Leaf Classifier/train.csv")
ylabels <- as.numeric(as.factor(train$species))-1
# Applying PCA
train<-train[,-(1:2)]
pca_train<-prcomp(train,scale=TRUE,center=T)
varEx<-as.data.frame(pca_train$sdev^2/sum(pca_train$sdev^2))
varEx<-cbind(c(1:192),cumsum(varEx[,1]))
colnames(varEx)<-c("Nmbr_PCs","Cum_Var")
VarianceExplanation<-varEx[seq(0,180,10),]
#Creating new attributes
rotate<-pca_train$rotation[,1:80]
trainFinal2<-as.matrix(scale(train,center = TRUE, scale=TRUE))%*%(rotate)
#Splitting the data
rows<-sample(1:990,700)
ytrain<-ylabels[rows]
ytest<-ylabels[-rows]
trainFinal2_train<-data.matrix(trainFinal2[rows,])
trainFinal2_test<-data.matrix(trainFinal2[-rows,])
#Create Dmatrix
xgbtrain <- xgb.DMatrix(data.matrix(trainFinal2_train), label=ytrain, missing=NA)#Using cross validation to obtain ideal parameters
xgbcv<-xgb.cv(data=xgbtrain,label=ytrain,nfold=5, booster= "gbtree",nrounds=200,eta=0.1,num_class=99,subsample=0.6,
colsample_bytree = 0.95,
objective='multi:softmax',eval_metric='merror')## Warning in xgb.get.DMatrix(data, label, missing): xgboost: label will be
## ignored.## [1] train-merror:0.890708+0.017056 test-merror:0.955771+0.026819
## [2] train-merror:0.722774+0.034388 test-merror:0.888642+0.019479
## [3] train-merror:0.582206+0.038596 test-merror:0.805491+0.028686
## [4] train-merror:0.516101+0.014051 test-merror:0.786897+0.027034
## [5] train-merror:0.436088+0.042842 test-merror:0.744098+0.036863
## [6] train-merror:0.381833+0.034861 test-merror:0.716933+0.046476
## [7] train-merror:0.335034+0.045719 test-merror:0.685421+0.056517
## [8] train-merror:0.293606+0.025482 test-merror:0.669704+0.044219
## [9] train-merror:0.255036+0.036533 test-merror:0.639711+0.043891
## [10] train-merror:0.213934+0.026707 test-merror:0.615596+0.039550
## [11] train-merror:0.198936+0.020666 test-merror:0.597012+0.039252
## [12] train-merror:0.176453+0.017038 test-merror:0.588582+0.037358
## [13] train-merror:0.154655+0.015936 test-merror:0.564263+0.025385
## [14] train-merror:0.125359+0.009486 test-merror:0.534352+0.033354
## [15] train-merror:0.112857+0.009663 test-merror:0.502961+0.034196
## [16] train-merror:0.094999+0.004829 test-merror:0.490001+0.042453
## [17] train-merror:0.084292+0.004086 test-merror:0.461569+0.033191
## [18] train-merror:0.076430+0.003660 test-merror:0.444343+0.033417
## [19] train-merror:0.067148+0.005883 test-merror:0.430015+0.031218
## [20] train-merror:0.057501+0.006910 test-merror:0.420045+0.033248
## [21] train-merror:0.050003+0.006310 test-merror:0.410166+0.035096
## [22] train-merror:0.048216+0.007672 test-merror:0.401564+0.037238
## [23] train-merror:0.044644+0.007413 test-merror:0.385726+0.044281
## [24] train-merror:0.040719+0.003865 test-merror:0.384256+0.044902
## [25] train-merror:0.036792+0.005182 test-merror:0.367141+0.030453
## [26] train-merror:0.032500+0.003806 test-merror:0.351446+0.036817
## [27] train-merror:0.028571+0.002516 test-merror:0.342812+0.029064
## [28] train-merror:0.023571+0.001326 test-merror:0.339883+0.025995
## [29] train-merror:0.022142+0.003840 test-merror:0.332770+0.024869
## [30] train-merror:0.021072+0.003078 test-merror:0.332790+0.024353
## [31] train-merror:0.018210+0.003042 test-merror:0.322779+0.024928
## [32] train-merror:0.016072+0.002521 test-merror:0.314186+0.024075
## [33] train-merror:0.013213+0.001810 test-merror:0.317013+0.029196
## [34] train-merror:0.012856+0.002071 test-merror:0.304175+0.026272
## [35] train-merror:0.012141+0.001318 test-merror:0.296971+0.038626
## [36] train-merror:0.011427+0.001808 test-merror:0.299889+0.039310
## [37] train-merror:0.010713+0.003385 test-merror:0.297052+0.040858
## [38] train-merror:0.007857+0.002427 test-merror:0.291306+0.037241
## [39] train-merror:0.007140+0.002514 test-merror:0.285621+0.035418
## [40] train-merror:0.006784+0.003458 test-merror:0.275712+0.034948
## [41] train-merror:0.006783+0.002356 test-merror:0.269946+0.029244
## [42] train-merror:0.005711+0.002359 test-merror:0.271415+0.032414
## [43] train-merror:0.005354+0.002516 test-merror:0.272833+0.031829
## [44] train-merror:0.004998+0.002849 test-merror:0.267109+0.027302
## [45] train-merror:0.004639+0.002891 test-merror:0.261404+0.031916
## [46] train-merror:0.003926+0.002074 test-merror:0.258587+0.025409
## [47] train-merror:0.003568+0.002249 test-merror:0.255740+0.023424
## [48] train-merror:0.003570+0.001593 test-merror:0.248647+0.028907
## [49] train-merror:0.003212+0.001744 test-merror:0.247249+0.028543
## [50] train-merror:0.003212+0.001744 test-merror:0.248647+0.028907
## [51] train-merror:0.003212+0.001744 test-merror:0.245800+0.027615
## [52] train-merror:0.002496+0.002418 test-merror:0.245769+0.025805
## [53] train-merror:0.002138+0.002077 test-merror:0.241473+0.029719
## [54] train-merror:0.001781+0.001949 test-merror:0.235769+0.034234
## [55] train-merror:0.001426+0.001332 test-merror:0.231524+0.029563
## [56] train-merror:0.001427+0.001333 test-merror:0.230074+0.025970
## [57] train-merror:0.001069+0.001424 test-merror:0.228727+0.030767
## [58] train-merror:0.001069+0.001424 test-merror:0.227340+0.031867
## [59] train-merror:0.000712+0.001424 test-merror:0.224493+0.031361
## [60] train-merror:0.000712+0.001424 test-merror:0.218778+0.032895
## [61] train-merror:0.000712+0.001424 test-merror:0.217318+0.028313
## [62] train-merror:0.000356+0.000712 test-merror:0.214451+0.023707
## [63] train-merror:0.000356+0.000712 test-merror:0.214461+0.025447
## [64] train-merror:0.000356+0.000712 test-merror:0.213032+0.024392
## [65] train-merror:0.000356+0.000712 test-merror:0.211624+0.025834
## [66] train-merror:0.000356+0.000712 test-merror:0.210205+0.029574
## [67] train-merror:0.000356+0.000712 test-merror:0.207337+0.028180
## [68] train-merror:0.000356+0.000712 test-merror:0.208827+0.032381
## [69] train-merror:0.000000+0.000000 test-merror:0.211726+0.036631
## [70] train-merror:0.000000+0.000000 test-merror:0.210297+0.035797
## [71] train-merror:0.000000+0.000000 test-merror:0.210287+0.034620
## [72] train-merror:0.000000+0.000000 test-merror:0.204500+0.027304
## [73] train-merror:0.000000+0.000000 test-merror:0.203061+0.027987
## [74] train-merror:0.000000+0.000000 test-merror:0.204542+0.034395
## [75] train-merror:0.000000+0.000000 test-merror:0.205970+0.032207
## [76] train-merror:0.000000+0.000000 test-merror:0.201674+0.032388
## [77] train-merror:0.000000+0.000000 test-merror:0.203133+0.031649
## [78] train-merror:0.000000+0.000000 test-merror:0.200276+0.033055
## [79] train-merror:0.000000+0.000000 test-merror:0.203113+0.031536
## [80] train-merror:0.000000+0.000000 test-merror:0.200266+0.031421
## [81] train-merror:0.000000+0.000000 test-merror:0.201725+0.037214
## [82] train-merror:0.000000+0.000000 test-merror:0.201736+0.040756
## [83] train-merror:0.000000+0.000000 test-merror:0.196031+0.039764
## [84] train-merror:0.000000+0.000000 test-merror:0.200307+0.036046
## [85] train-merror:0.000000+0.000000 test-merror:0.200307+0.036046
## [86] train-merror:0.000000+0.000000 test-merror:0.195949+0.031834
## [87] train-merror:0.000000+0.000000 test-merror:0.197368+0.031122
## [88] train-merror:0.000000+0.000000 test-merror:0.194500+0.027055
## [89] train-merror:0.000000+0.000000 test-merror:0.194521+0.027183
## [90] train-merror:0.000000+0.000000 test-merror:0.196001+0.033738
## [91] train-merror:0.000000+0.000000 test-merror:0.194562+0.034239
## [92] train-merror:0.000000+0.000000 test-merror:0.198889+0.039816
## [93] train-merror:0.000000+0.000000 test-merror:0.196021+0.038175
## [94] train-merror:0.000000+0.000000 test-merror:0.196021+0.038175
## [95] train-merror:0.000000+0.000000 test-merror:0.191674+0.030100
## [96] train-merror:0.000000+0.000000 test-merror:0.190245+0.029840
## [97] train-merror:0.000000+0.000000 test-merror:0.193102+0.028210
## [98] train-merror:0.000000+0.000000 test-merror:0.191663+0.028662
## [99] train-merror:0.000000+0.000000 test-merror:0.191684+0.031581
## [100] train-merror:0.000000+0.000000 test-merror:0.190235+0.028745
## [101] train-merror:0.000000+0.000000 test-merror:0.188785+0.025921
## [102] train-merror:0.000000+0.000000 test-merror:0.184499+0.025763
## [103] train-merror:0.000000+0.000000 test-merror:0.185948+0.028452
## [104] train-merror:0.000000+0.000000 test-merror:0.185948+0.028452
## [105] train-merror:0.000000+0.000000 test-merror:0.184499+0.025763
## [106] train-merror:0.000000+0.000000 test-merror:0.183071+0.029626
## [107] train-merror:0.000000+0.000000 test-merror:0.180183+0.027899
## [108] train-merror:0.000000+0.000000 test-merror:0.180183+0.027899
## [109] train-merror:0.000000+0.000000 test-merror:0.181632+0.030604
## [110] train-merror:0.000000+0.000000 test-merror:0.183050+0.023126
## [111] train-merror:0.000000+0.000000 test-merror:0.185948+0.028452
## [112] train-merror:0.000000+0.000000 test-merror:0.184530+0.030042
## [113] train-merror:0.000000+0.000000 test-merror:0.184530+0.030042
## [114] train-merror:0.000000+0.000000 test-merror:0.184530+0.030042
## [115] train-merror:0.000000+0.000000 test-merror:0.185969+0.029608
## [116] train-merror:0.000000+0.000000 test-merror:0.180213+0.027696
## [117] train-merror:0.000000+0.000000 test-merror:0.180223+0.027008
## [118] train-merror:0.000000+0.000000 test-merror:0.178785+0.027559
## [119] train-merror:0.000000+0.000000 test-merror:0.180223+0.027008
## [120] train-merror:0.000000+0.000000 test-merror:0.175876+0.018864
## [121] train-merror:0.000000+0.000000 test-merror:0.177325+0.021531
## [122] train-merror:0.000000+0.000000 test-merror:0.175876+0.018864
## [123] train-merror:0.000000+0.000000 test-merror:0.175876+0.018864
## [124] train-merror:0.000000+0.000000 test-merror:0.174426+0.016276
## [125] train-merror:0.000000+0.000000 test-merror:0.177314+0.018838
## [126] train-merror:0.000000+0.000000 test-merror:0.177314+0.018838
## [127] train-merror:0.000000+0.000000 test-merror:0.175876+0.018864
## [128] train-merror:0.000000+0.000000 test-merror:0.177314+0.018838
## [129] train-merror:0.000000+0.000000 test-merror:0.175865+0.016374
## [130] train-merror:0.000000+0.000000 test-merror:0.174437+0.016388
## [131] train-merror:0.000000+0.000000 test-merror:0.175886+0.018959
## [132] train-merror:0.000000+0.000000 test-merror:0.177335+0.021614
## [133] train-merror:0.000000+0.000000 test-merror:0.177314+0.018838
## [134] train-merror:0.000000+0.000000 test-merror:0.174447+0.018876
## [135] train-merror:0.000000+0.000000 test-merror:0.175896+0.021637
## [136] train-merror:0.000000+0.000000 test-merror:0.175896+0.021637
## [137] train-merror:0.000000+0.000000 test-merror:0.175927+0.022767
## [138] train-merror:0.000000+0.000000 test-merror:0.171590+0.017327
## [139] train-merror:0.000000+0.000000 test-merror:0.173039+0.019984
## [140] train-merror:0.000000+0.000000 test-merror:0.173039+0.019984
## [141] train-merror:0.000000+0.000000 test-merror:0.171590+0.017327
## [142] train-merror:0.000000+0.000000 test-merror:0.170161+0.017574
## [143] train-merror:0.000000+0.000000 test-merror:0.170151+0.018056
## [144] train-merror:0.000000+0.000000 test-merror:0.170192+0.021550
## [145] train-merror:0.000000+0.000000 test-merror:0.168743+0.018894
## [146] train-merror:0.000000+0.000000 test-merror:0.171620+0.021347
## [147] train-merror:0.000000+0.000000 test-merror:0.171590+0.020638
## [148] train-merror:0.000000+0.000000 test-merror:0.173039+0.019984
## [149] train-merror:0.000000+0.000000 test-merror:0.170151+0.017476
## [150] train-merror:0.000000+0.000000 test-merror:0.167283+0.016214
## [151] train-merror:0.000000+0.000000 test-merror:0.167283+0.016214
## [152] train-merror:0.000000+0.000000 test-merror:0.168701+0.014795
## [153] train-merror:0.000000+0.000000 test-merror:0.171590+0.017327
## [154] train-merror:0.000000+0.000000 test-merror:0.168753+0.021577
## [155] train-merror:0.000000+0.000000 test-merror:0.170151+0.017476
## [156] train-merror:0.000000+0.000000 test-merror:0.167273+0.014808
## [157] train-merror:0.000000+0.000000 test-merror:0.165854+0.016100
## [158] train-merror:0.000000+0.000000 test-merror:0.165844+0.016625
## [159] train-merror:0.000000+0.000000 test-merror:0.164415+0.016389
## [160] train-merror:0.000000+0.000000 test-merror:0.165824+0.015806
## [161] train-merror:0.000000+0.000000 test-merror:0.164395+0.016198
## [162] train-merror:0.000000+0.000000 test-merror:0.165824+0.015806
## [163] train-merror:0.000000+0.000000 test-merror:0.164415+0.016389
## [164] train-merror:0.000000+0.000000 test-merror:0.164415+0.016389
## [165] train-merror:0.000000+0.000000 test-merror:0.167293+0.016321
## [166] train-merror:0.000000+0.000000 test-merror:0.164426+0.016488
## [167] train-merror:0.000000+0.000000 test-merror:0.164426+0.016488
## [168] train-merror:0.000000+0.000000 test-merror:0.165865+0.016827
## [169] train-merror:0.000000+0.000000 test-merror:0.168702+0.015476
## [170] train-merror:0.000000+0.000000 test-merror:0.167263+0.015375
## [171] train-merror:0.000000+0.000000 test-merror:0.167263+0.015375
## [172] train-merror:0.000000+0.000000 test-merror:0.167273+0.016133
## [173] train-merror:0.000000+0.000000 test-merror:0.168702+0.015476
## [174] train-merror:0.000000+0.000000 test-merror:0.167273+0.016133
## [175] train-merror:0.000000+0.000000 test-merror:0.167263+0.015375
## [176] train-merror:0.000000+0.000000 test-merror:0.165834+0.015907
## [177] train-merror:0.000000+0.000000 test-merror:0.168702+0.015476
## [178] train-merror:0.000000+0.000000 test-merror:0.168681+0.015915
## [179] train-merror:0.000000+0.000000 test-merror:0.170110+0.015115
## [180] train-merror:0.000000+0.000000 test-merror:0.170131+0.018435
## [181] train-merror:0.000000+0.000000 test-merror:0.170131+0.018435
## [182] train-merror:0.000000+0.000000 test-merror:0.168722+0.018733
## [183] train-merror:0.000000+0.000000 test-merror:0.170151+0.018056
## [184] train-merror:0.000000+0.000000 test-merror:0.168722+0.018733
## [185] train-merror:0.000000+0.000000 test-merror:0.168722+0.018733
## [186] train-merror:0.000000+0.000000 test-merror:0.168722+0.018733
## [187] train-merror:0.000000+0.000000 test-merror:0.170151+0.018056
## [188] train-merror:0.000000+0.000000 test-merror:0.170151+0.018056
## [189] train-merror:0.000000+0.000000 test-merror:0.170151+0.018056
## [190] train-merror:0.000000+0.000000 test-merror:0.171580+0.017816
## [191] train-merror:0.000000+0.000000 test-merror:0.171549+0.017536
## [192] train-merror:0.000000+0.000000 test-merror:0.171559+0.019788
## [193] train-merror:0.000000+0.000000 test-merror:0.167274+0.020123
## [194] train-merror:0.000000+0.000000 test-merror:0.167294+0.019779
## [195] train-merror:0.000000+0.000000 test-merror:0.165865+0.020199
## [196] train-merror:0.000000+0.000000 test-merror:0.167294+0.019779
## [197] train-merror:0.000000+0.000000 test-merror:0.165865+0.020199
## [198] train-merror:0.000000+0.000000 test-merror:0.167294+0.019779
## [199] train-merror:0.000000+0.000000 test-merror:0.167314+0.022885
## [200] train-merror:0.000000+0.000000 test-merror:0.168743+0.022424
#Running training model
model<-xgb.train(data=xgbtrain,label=ytrain, booster= "gbtree",nrounds=100,eta=0.1,num_class=99,subsample=0.6,
colsample_bytree = 0.95,
objective='multi:softmax',eval_metric='merror')
# Prediict
PRED_train <- predict(model, xgbtrain)
confusionMatrix(PRED_train, ytrain)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 1 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0
## 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0
## 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0
## 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0
## 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0
## 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0
## 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6
## 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 53 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 57 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 58 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 60 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 61 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 62 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 63 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 64 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 65 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 66 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 67 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 68 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 69 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 70 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0
## 71 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0
## 72 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0
## 73 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0
## 74 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0
## 75 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0
## 76 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0
## 77 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0
## 78 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0
## 79 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0
## 80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0
## 81 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0
## 82 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0
## 83 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7
## 84 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 85 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 87 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 90 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 93 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 96 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 97 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 98 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Reference
## Prediction 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 41 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 42 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 44 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 47 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 53 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 57 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 58 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 60 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 61 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 62 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 66 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 67 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 68 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 69 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 70 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 71 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 72 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 73 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 74 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 76 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 77 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 78 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 79 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 81 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 83 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 84 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 85 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0
## 86 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0
## 87 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0
## 89 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0
## 90 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0
## 91 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0
## 93 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0
## 94 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0
## 95 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0
## 96 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0
## 97 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0
## 98 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9947, 1)
## No Information Rate : 0.0143
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 0 Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 1.000000 1.00000 1.00000 1.00000 1.00 1.00
## Specificity 1.000000 1.00000 1.00000 1.00000 1.00 1.00
## Pos Pred Value 1.000000 1.00000 1.00000 1.00000 1.00 1.00
## Neg Pred Value 1.000000 1.00000 1.00000 1.00000 1.00 1.00
## Prevalence 0.007143 0.01143 0.01143 0.01143 0.01 0.01
## Detection Rate 0.007143 0.01143 0.01143 0.01143 0.01 0.01
## Detection Prevalence 0.007143 0.01143 0.01143 0.01143 0.01 0.01
## Balanced Accuracy 1.000000 1.00000 1.00000 1.00000 1.00 1.00
## Class: 6 Class: 7 Class: 8 Class: 9 Class: 10
## Sensitivity 1.00000 1.00000 1.000000 1.00000 1.000000
## Specificity 1.00000 1.00000 1.000000 1.00000 1.000000
## Pos Pred Value 1.00000 1.00000 1.000000 1.00000 1.000000
## Neg Pred Value 1.00000 1.00000 1.000000 1.00000 1.000000
## Prevalence 0.01143 0.01143 0.007143 0.01286 0.008571
## Detection Rate 0.01143 0.01143 0.007143 0.01286 0.008571
## Detection Prevalence 0.01143 0.01143 0.007143 0.01286 0.008571
## Balanced Accuracy 1.00000 1.00000 1.000000 1.00000 1.000000
## Class: 11 Class: 12 Class: 13 Class: 14 Class: 15
## Sensitivity 1.000000 1.000000 1.00000 1.00000 1.00000
## Specificity 1.000000 1.000000 1.00000 1.00000 1.00000
## Pos Pred Value 1.000000 1.000000 1.00000 1.00000 1.00000
## Neg Pred Value 1.000000 1.000000 1.00000 1.00000 1.00000
## Prevalence 0.007143 0.008571 0.01286 0.01143 0.01286
## Detection Rate 0.007143 0.008571 0.01286 0.01143 0.01286
## Detection Prevalence 0.007143 0.008571 0.01286 0.01143 0.01286
## Balanced Accuracy 1.000000 1.000000 1.00000 1.00000 1.00000
## Class: 16 Class: 17 Class: 18 Class: 19 Class: 20
## Sensitivity 1.000000 1.00000 1.00000 1.000000 1.000000
## Specificity 1.000000 1.00000 1.00000 1.000000 1.000000
## Pos Pred Value 1.000000 1.00000 1.00000 1.000000 1.000000
## Neg Pred Value 1.000000 1.00000 1.00000 1.000000 1.000000
## Prevalence 0.008571 0.01143 0.01143 0.008571 0.008571
## Detection Rate 0.008571 0.01143 0.01143 0.008571 0.008571
## Detection Prevalence 0.008571 0.01143 0.01143 0.008571 0.008571
## Balanced Accuracy 1.000000 1.00000 1.00000 1.000000 1.000000
## Class: 21 Class: 22 Class: 23 Class: 24 Class: 25
## Sensitivity 1.000000 1.00 1.000000 1.000000 1.00
## Specificity 1.000000 1.00 1.000000 1.000000 1.00
## Pos Pred Value 1.000000 1.00 1.000000 1.000000 1.00
## Neg Pred Value 1.000000 1.00 1.000000 1.000000 1.00
## Prevalence 0.008571 0.01 0.008571 0.008571 0.01
## Detection Rate 0.008571 0.01 0.008571 0.008571 0.01
## Detection Prevalence 0.008571 0.01 0.008571 0.008571 0.01
## Balanced Accuracy 1.000000 1.00 1.000000 1.000000 1.00
## Class: 26 Class: 27 Class: 28 Class: 29 Class: 30
## Sensitivity 1.00000 1.00000 1.00000 1.000000 1.00000
## Specificity 1.00000 1.00000 1.00000 1.000000 1.00000
## Pos Pred Value 1.00000 1.00000 1.00000 1.000000 1.00000
## Neg Pred Value 1.00000 1.00000 1.00000 1.000000 1.00000
## Prevalence 0.01143 0.01143 0.01286 0.008571 0.01143
## Detection Rate 0.01143 0.01143 0.01286 0.008571 0.01143
## Detection Prevalence 0.01143 0.01143 0.01286 0.008571 0.01143
## Balanced Accuracy 1.00000 1.00000 1.00000 1.000000 1.00000
## Class: 31 Class: 32 Class: 33 Class: 34 Class: 35
## Sensitivity 1.000000 1.00 1.00000 1.000000 1.00
## Specificity 1.000000 1.00 1.00000 1.000000 1.00
## Pos Pred Value 1.000000 1.00 1.00000 1.000000 1.00
## Neg Pred Value 1.000000 1.00 1.00000 1.000000 1.00
## Prevalence 0.008571 0.01 0.01286 0.008571 0.01
## Detection Rate 0.008571 0.01 0.01286 0.008571 0.01
## Detection Prevalence 0.008571 0.01 0.01286 0.008571 0.01
## Balanced Accuracy 1.000000 1.00 1.00000 1.000000 1.00
## Class: 36 Class: 37 Class: 38 Class: 39 Class: 40
## Sensitivity 1.000000 1.000000 1.00000 1.00 1.00000
## Specificity 1.000000 1.000000 1.00000 1.00 1.00000
## Pos Pred Value 1.000000 1.000000 1.00000 1.00 1.00000
## Neg Pred Value 1.000000 1.000000 1.00000 1.00 1.00000
## Prevalence 0.008571 0.008571 0.01286 0.01 0.01286
## Detection Rate 0.008571 0.008571 0.01286 0.01 0.01286
## Detection Prevalence 0.008571 0.008571 0.01286 0.01 0.01286
## Balanced Accuracy 1.000000 1.000000 1.00000 1.00 1.00000
## Class: 41 Class: 42 Class: 43 Class: 44 Class: 45
## Sensitivity 1.000000 1.000000 1.000000 1.00 1.00
## Specificity 1.000000 1.000000 1.000000 1.00 1.00
## Pos Pred Value 1.000000 1.000000 1.000000 1.00 1.00
## Neg Pred Value 1.000000 1.000000 1.000000 1.00 1.00
## Prevalence 0.008571 0.007143 0.007143 0.01 0.01
## Detection Rate 0.008571 0.007143 0.007143 0.01 0.01
## Detection Prevalence 0.008571 0.007143 0.007143 0.01 0.01
## Balanced Accuracy 1.000000 1.000000 1.000000 1.00 1.00
## Class: 46 Class: 47 Class: 48 Class: 49 Class: 50
## Sensitivity 1.00000 1.00000 1.00 1.000000 1.00000
## Specificity 1.00000 1.00000 1.00 1.000000 1.00000
## Pos Pred Value 1.00000 1.00000 1.00 1.000000 1.00000
## Neg Pred Value 1.00000 1.00000 1.00 1.000000 1.00000
## Prevalence 0.01143 0.01286 0.01 0.008571 0.01143
## Detection Rate 0.01143 0.01286 0.01 0.008571 0.01143
## Detection Prevalence 0.01143 0.01286 0.01 0.008571 0.01143
## Balanced Accuracy 1.00000 1.00000 1.00 1.000000 1.00000
## Class: 51 Class: 52 Class: 53 Class: 54 Class: 55
## Sensitivity 1.000000 1.00000 1.00000 1.00000 1.00000
## Specificity 1.000000 1.00000 1.00000 1.00000 1.00000
## Pos Pred Value 1.000000 1.00000 1.00000 1.00000 1.00000
## Neg Pred Value 1.000000 1.00000 1.00000 1.00000 1.00000
## Prevalence 0.008571 0.01143 0.01143 0.01143 0.01286
## Detection Rate 0.008571 0.01143 0.01143 0.01143 0.01286
## Detection Prevalence 0.008571 0.01143 0.01143 0.01143 0.01286
## Balanced Accuracy 1.000000 1.00000 1.00000 1.00000 1.00000
## Class: 56 Class: 57 Class: 58 Class: 59 Class: 60
## Sensitivity 1.00 1.00000 1.00000 1.00000 1.00
## Specificity 1.00 1.00000 1.00000 1.00000 1.00
## Pos Pred Value 1.00 1.00000 1.00000 1.00000 1.00
## Neg Pred Value 1.00 1.00000 1.00000 1.00000 1.00
## Prevalence 0.01 0.01143 0.01429 0.01143 0.01
## Detection Rate 0.01 0.01143 0.01429 0.01143 0.01
## Detection Prevalence 0.01 0.01143 0.01429 0.01143 0.01
## Balanced Accuracy 1.00 1.00000 1.00000 1.00000 1.00
## Class: 61 Class: 62 Class: 63 Class: 64 Class: 65
## Sensitivity 1.000000 1.00 1.000000 1.000000 1.00
## Specificity 1.000000 1.00 1.000000 1.000000 1.00
## Pos Pred Value 1.000000 1.00 1.000000 1.000000 1.00
## Neg Pred Value 1.000000 1.00 1.000000 1.000000 1.00
## Prevalence 0.007143 0.01 0.007143 0.008571 0.01
## Detection Rate 0.007143 0.01 0.007143 0.008571 0.01
## Detection Prevalence 0.007143 0.01 0.007143 0.008571 0.01
## Balanced Accuracy 1.000000 1.00 1.000000 1.000000 1.00
## Class: 66 Class: 67 Class: 68 Class: 69 Class: 70
## Sensitivity 1.00000 1.000000 1.00000 1.00 1.00
## Specificity 1.00000 1.000000 1.00000 1.00 1.00
## Pos Pred Value 1.00000 1.000000 1.00000 1.00 1.00
## Neg Pred Value 1.00000 1.000000 1.00000 1.00 1.00
## Prevalence 0.01286 0.007143 0.01143 0.01 0.01
## Detection Rate 0.01286 0.007143 0.01143 0.01 0.01
## Detection Prevalence 0.01286 0.007143 0.01143 0.01 0.01
## Balanced Accuracy 1.00000 1.000000 1.00000 1.00 1.00
## Class: 71 Class: 72 Class: 73 Class: 74 Class: 75
## Sensitivity 1.000000 1.00 1.00 1.00 1.00000
## Specificity 1.000000 1.00 1.00 1.00 1.00000
## Pos Pred Value 1.000000 1.00 1.00 1.00 1.00000
## Neg Pred Value 1.000000 1.00 1.00 1.00 1.00000
## Prevalence 0.008571 0.01 0.01 0.01 0.01286
## Detection Rate 0.008571 0.01 0.01 0.01 0.01286
## Detection Prevalence 0.008571 0.01 0.01 0.01 0.01286
## Balanced Accuracy 1.000000 1.00 1.00 1.00 1.00000
## Class: 76 Class: 77 Class: 78 Class: 79 Class: 80
## Sensitivity 1.00000 1.000000 1.00000 1.00000 1.00000
## Specificity 1.00000 1.000000 1.00000 1.00000 1.00000
## Pos Pred Value 1.00000 1.000000 1.00000 1.00000 1.00000
## Neg Pred Value 1.00000 1.000000 1.00000 1.00000 1.00000
## Prevalence 0.01143 0.008571 0.01143 0.01143 0.01143
## Detection Rate 0.01143 0.008571 0.01143 0.01143 0.01143
## Detection Prevalence 0.01143 0.008571 0.01143 0.01143 0.01143
## Balanced Accuracy 1.00000 1.000000 1.00000 1.00000 1.00000
## Class: 81 Class: 82 Class: 83 Class: 84 Class: 85
## Sensitivity 1.00000 1.00 1.00 1.000000 1.000000
## Specificity 1.00000 1.00 1.00 1.000000 1.000000
## Pos Pred Value 1.00000 1.00 1.00 1.000000 1.000000
## Neg Pred Value 1.00000 1.00 1.00 1.000000 1.000000
## Prevalence 0.01143 0.01 0.01 0.005714 0.007143
## Detection Rate 0.01143 0.01 0.01 0.005714 0.007143
## Detection Prevalence 0.01143 0.01 0.01 0.005714 0.007143
## Balanced Accuracy 1.00000 1.00 1.00 1.000000 1.000000
## Class: 86 Class: 87 Class: 88 Class: 89 Class: 90
## Sensitivity 1.000000 1.00000 1.000000 1.00 1.00
## Specificity 1.000000 1.00000 1.000000 1.00 1.00
## Pos Pred Value 1.000000 1.00000 1.000000 1.00 1.00
## Neg Pred Value 1.000000 1.00000 1.000000 1.00 1.00
## Prevalence 0.008571 0.01143 0.008571 0.01 0.01
## Detection Rate 0.008571 0.01143 0.008571 0.01 0.01
## Detection Prevalence 0.008571 0.01143 0.008571 0.01 0.01
## Balanced Accuracy 1.000000 1.00000 1.000000 1.00 1.00
## Class: 91 Class: 92 Class: 93 Class: 94 Class: 95
## Sensitivity 1.00 1.000000 1.00 1.00000 1.00000
## Specificity 1.00 1.000000 1.00 1.00000 1.00000
## Pos Pred Value 1.00 1.000000 1.00 1.00000 1.00000
## Neg Pred Value 1.00 1.000000 1.00 1.00000 1.00000
## Prevalence 0.01 0.007143 0.01 0.01143 0.01286
## Detection Rate 0.01 0.007143 0.01 0.01143 0.01286
## Detection Prevalence 0.01 0.007143 0.01 0.01143 0.01286
## Balanced Accuracy 1.00 1.000000 1.00 1.00000 1.00000
## Class: 96 Class: 97 Class: 98
## Sensitivity 1.00 1.00 1.000000
## Specificity 1.00 1.00 1.000000
## Pos Pred Value 1.00 1.00 1.000000
## Neg Pred Value 1.00 1.00 1.000000
## Prevalence 0.01 0.01 0.007143
## Detection Rate 0.01 0.01 0.007143
## Detection Prevalence 0.01 0.01 0.007143
## Balanced Accuracy 1.00 1.00 1.000000PRED_test <- predict(model,trainFinal2_test )
confusionMatrix(PRED_test, ytest)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 1 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0
## 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0
## 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0
## 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0
## 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4
## 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 53 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 57 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 59 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 60 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 61 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 62 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 65 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 66 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 67 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 68 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 69 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 70 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 71 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 72 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 73 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 74 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 75 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## 76 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0
## 77 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0
## 78 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0
## 79 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0
## 80 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0
## 81 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0
## 83 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0
## 84 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0
## 85 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0
## 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0
## 87 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
## 88 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 90 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 91 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 93 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 96 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 97 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 98 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## Reference
## Prediction 88 89 90 91 92 93 94 95 96 97 98
## 0 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0 0 0 0 0
## 18 0 0 0 0 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0 0 0 0 0
## 20 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0
## 24 0 0 0 0 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 0 0 0 0 0
## 26 0 0 0 0 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 1 0 0 0 0 0 0
## 29 0 0 0 0 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0 0 0 0 1
## 31 0 0 0 0 1 0 0 0 0 0 0
## 32 0 0 0 0 0 0 0 0 0 0 0
## 33 0 0 0 0 0 0 0 0 0 0 0
## 34 0 0 0 0 0 0 0 0 0 0 0
## 35 0 0 0 0 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0 0 0 0 0
## 37 0 0 0 0 0 0 0 0 0 0 0
## 38 0 0 0 0 0 0 0 0 0 0 0
## 39 0 0 0 0 0 0 0 0 0 0 0
## 40 0 0 0 0 0 0 0 0 0 0 0
## 41 0 0 0 0 0 0 0 0 0 0 0
## 42 0 0 0 0 0 0 0 0 0 0 0
## 43 0 0 0 0 0 0 0 0 0 0 0
## 44 0 0 0 0 0 0 0 0 0 0 0
## 45 0 0 0 0 0 0 0 0 0 0 0
## 46 0 0 0 0 0 0 0 0 0 0 0
## 47 0 0 0 0 0 0 0 0 0 0 0
## 48 0 1 0 0 0 0 0 0 0 0 0
## 49 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 0 0 0
## 51 0 0 0 0 0 0 0 0 0 0 0
## 52 0 0 0 0 0 0 0 0 0 0 0
## 53 0 0 0 0 0 0 0 0 0 0 0
## 54 0 0 0 0 0 0 0 0 0 0 1
## 55 0 0 0 0 0 0 0 0 0 0 0
## 56 0 0 0 0 0 0 0 0 0 0 0
## 57 0 0 0 0 0 0 0 0 0 0 0
## 59 0 0 0 0 0 0 0 0 0 0 0
## 60 0 0 0 0 0 0 0 0 0 0 0
## 61 0 0 0 0 0 0 0 0 0 0 0
## 62 0 0 0 0 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 0 0 0
## 64 0 0 0 0 0 0 0 0 0 0 0
## 65 0 0 0 0 0 0 0 0 0 0 0
## 66 0 0 0 0 0 0 0 0 0 0 0
## 67 0 0 0 0 0 0 0 0 0 0 0
## 68 0 0 0 0 0 0 0 0 0 0 0
## 69 0 0 0 0 0 0 0 0 0 0 0
## 70 0 0 0 0 0 0 0 0 0 0 0
## 71 0 0 0 0 0 0 0 0 0 0 0
## 72 1 0 0 0 0 0 0 0 0 0 0
## 73 0 0 0 0 0 0 0 0 0 0 0
## 74 0 0 0 0 0 0 0 0 0 0 0
## 75 0 0 0 0 0 0 0 0 0 0 0
## 76 0 0 0 0 0 0 0 0 0 0 0
## 77 0 0 0 0 0 0 0 0 0 0 0
## 78 0 0 0 0 0 0 0 0 0 0 0
## 79 0 0 0 0 0 0 0 0 0 0 0
## 80 0 0 0 0 0 0 0 0 0 0 0
## 81 0 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0 0
## 83 0 0 0 0 0 0 0 0 0 0 0
## 84 0 0 0 0 0 0 0 0 0 0 0
## 85 0 0 0 0 0 0 0 0 0 0 0
## 86 0 0 0 0 0 0 0 0 0 0 0
## 87 0 0 0 0 0 0 0 0 0 0 0
## 88 3 0 0 0 0 0 0 0 0 0 0
## 89 0 2 0 0 0 0 0 0 0 0 0
## 90 0 0 3 0 0 0 0 0 0 0 0
## 91 0 0 0 3 0 0 0 0 0 0 0
## 92 0 0 0 0 3 0 0 0 0 0 0
## 93 0 0 0 0 0 3 0 0 0 0 0
## 94 0 0 0 0 0 0 2 0 0 0 0
## 95 0 0 0 0 0 0 0 1 0 0 0
## 96 0 0 0 0 0 0 0 0 3 0 0
## 97 0 0 0 0 0 0 0 0 0 3 0
## 98 0 0 0 0 0 0 0 0 0 0 3
##
## Overall Statistics
##
## Accuracy : 0.8931
## 95% CI : (0.8517, 0.9262)
## No Information Rate : 0.0207
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8919
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 0 Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 1.00000 1.000000 1.000000 1.000000 1.00000 1.00000
## Specificity 1.00000 0.996528 1.000000 0.996528 1.00000 1.00000
## Pos Pred Value 1.00000 0.666667 1.000000 0.666667 1.00000 1.00000
## Neg Pred Value 1.00000 1.000000 1.000000 1.000000 1.00000 1.00000
## Prevalence 0.01724 0.006897 0.006897 0.006897 0.01034 0.01034
## Detection Rate 0.01724 0.006897 0.006897 0.006897 0.01034 0.01034
## Detection Prevalence 0.01724 0.010345 0.006897 0.010345 0.01034 0.01034
## Balanced Accuracy 1.00000 0.998264 1.000000 0.998264 1.00000 1.00000
## Class: 6 Class: 7 Class: 8 Class: 9 Class: 10
## Sensitivity 1.000000 1.000000 0.60000 1.000000 0.75000
## Specificity 1.000000 1.000000 1.00000 1.000000 1.00000
## Pos Pred Value 1.000000 1.000000 1.00000 1.000000 1.00000
## Neg Pred Value 1.000000 1.000000 0.99303 1.000000 0.99652
## Prevalence 0.006897 0.006897 0.01724 0.003448 0.01379
## Detection Rate 0.006897 0.006897 0.01034 0.003448 0.01034
## Detection Prevalence 0.006897 0.006897 0.01034 0.003448 0.01034
## Balanced Accuracy 1.000000 1.000000 0.80000 1.000000 0.87500
## Class: 11 Class: 12 Class: 13 Class: 14 Class: 15
## Sensitivity 0.60000 1.00000 1.000000 1.000000 1.000000
## Specificity 1.00000 1.00000 1.000000 1.000000 1.000000
## Pos Pred Value 1.00000 1.00000 1.000000 1.000000 1.000000
## Neg Pred Value 0.99303 1.00000 1.000000 1.000000 1.000000
## Prevalence 0.01724 0.01379 0.003448 0.006897 0.003448
## Detection Rate 0.01034 0.01379 0.003448 0.006897 0.003448
## Detection Prevalence 0.01034 0.01379 0.003448 0.006897 0.003448
## Balanced Accuracy 0.80000 1.00000 1.000000 1.000000 1.000000
## Class: 16 Class: 17 Class: 18 Class: 19 Class: 20
## Sensitivity 1.00000 1.000000 0.500000 0.500000 1.00000
## Specificity 1.00000 1.000000 1.000000 1.000000 1.00000
## Pos Pred Value 1.00000 1.000000 1.000000 1.000000 1.00000
## Neg Pred Value 1.00000 1.000000 0.996540 0.993056 1.00000
## Prevalence 0.01379 0.006897 0.006897 0.013793 0.01379
## Detection Rate 0.01379 0.006897 0.003448 0.006897 0.01379
## Detection Prevalence 0.01379 0.006897 0.003448 0.006897 0.01379
## Balanced Accuracy 1.00000 1.000000 0.750000 0.750000 1.00000
## Class: 21 Class: 22 Class: 23 Class: 24 Class: 25
## Sensitivity 1.00000 1.00000 1.00000 1.00000 1.00000
## Specificity 1.00000 0.99652 1.00000 1.00000 0.99303
## Pos Pred Value 1.00000 0.75000 1.00000 1.00000 0.60000
## Neg Pred Value 1.00000 1.00000 1.00000 1.00000 1.00000
## Prevalence 0.01379 0.01034 0.01379 0.01379 0.01034
## Detection Rate 0.01379 0.01034 0.01379 0.01379 0.01034
## Detection Prevalence 0.01379 0.01379 0.01379 0.01379 0.01724
## Balanced Accuracy 1.00000 0.99826 1.00000 1.00000 0.99652
## Class: 26 Class: 27 Class: 28 Class: 29 Class: 30
## Sensitivity 0.500000 0.500000 1.000000 1.00000 1.000000
## Specificity 0.993056 1.000000 0.996540 1.00000 0.996528
## Pos Pred Value 0.333333 1.000000 0.500000 1.00000 0.666667
## Neg Pred Value 0.996516 0.996540 1.000000 1.00000 1.000000
## Prevalence 0.006897 0.006897 0.003448 0.01379 0.006897
## Detection Rate 0.003448 0.003448 0.003448 0.01379 0.006897
## Detection Prevalence 0.010345 0.003448 0.006897 0.01379 0.010345
## Balanced Accuracy 0.746528 0.750000 0.998270 1.00000 0.998264
## Class: 31 Class: 32 Class: 33 Class: 34 Class: 35
## Sensitivity 0.500000 1.00000 1.000000 0.75000 1.00000
## Specificity 0.993007 0.99652 1.000000 1.00000 1.00000
## Pos Pred Value 0.500000 0.75000 1.000000 1.00000 1.00000
## Neg Pred Value 0.993007 1.00000 1.000000 0.99652 1.00000
## Prevalence 0.013793 0.01034 0.003448 0.01379 0.01034
## Detection Rate 0.006897 0.01034 0.003448 0.01034 0.01034
## Detection Prevalence 0.013793 0.01379 0.003448 0.01034 0.01034
## Balanced Accuracy 0.746503 0.99826 1.000000 0.87500 1.00000
## Class: 36 Class: 37 Class: 38 Class: 39 Class: 40
## Sensitivity 1.00000 1.00000 1.000000 1.00000 1.000000
## Specificity 1.00000 1.00000 1.000000 1.00000 1.000000
## Pos Pred Value 1.00000 1.00000 1.000000 1.00000 1.000000
## Neg Pred Value 1.00000 1.00000 1.000000 1.00000 1.000000
## Prevalence 0.01379 0.01379 0.003448 0.01034 0.003448
## Detection Rate 0.01379 0.01379 0.003448 0.01034 0.003448
## Detection Prevalence 0.01379 0.01379 0.003448 0.01034 0.003448
## Balanced Accuracy 1.00000 1.00000 1.000000 1.00000 1.000000
## Class: 41 Class: 42 Class: 43 Class: 44 Class: 45
## Sensitivity 0.75000 1.00000 1.00000 0.666667 1.00000
## Specificity 0.99650 0.99649 1.00000 0.996516 1.00000
## Pos Pred Value 0.75000 0.83333 1.00000 0.666667 1.00000
## Neg Pred Value 0.99650 1.00000 1.00000 0.996516 1.00000
## Prevalence 0.01379 0.01724 0.01724 0.010345 0.01034
## Detection Rate 0.01034 0.01724 0.01724 0.006897 0.01034
## Detection Prevalence 0.01379 0.02069 0.01724 0.010345 0.01034
## Balanced Accuracy 0.87325 0.99825 1.00000 0.831591 1.00000
## Class: 46 Class: 47 Class: 48 Class: 49 Class: 50
## Sensitivity 1.000000 1.000000 1.00000 1.00000 1.000000
## Specificity 1.000000 1.000000 0.99303 1.00000 0.996528
## Pos Pred Value 1.000000 1.000000 0.60000 1.00000 0.666667
## Neg Pred Value 1.000000 1.000000 1.00000 1.00000 1.000000
## Prevalence 0.006897 0.003448 0.01034 0.01379 0.006897
## Detection Rate 0.006897 0.003448 0.01034 0.01379 0.006897
## Detection Prevalence 0.006897 0.003448 0.01724 0.01379 0.010345
## Balanced Accuracy 1.000000 1.000000 0.99652 1.00000 0.998264
## Class: 51 Class: 52 Class: 53 Class: 54 Class: 55
## Sensitivity 0.75000 1.000000 1.000000 1.000000 1.000000
## Specificity 1.00000 1.000000 1.000000 0.996528 1.000000
## Pos Pred Value 1.00000 1.000000 1.000000 0.666667 1.000000
## Neg Pred Value 0.99652 1.000000 1.000000 1.000000 1.000000
## Prevalence 0.01379 0.006897 0.006897 0.006897 0.003448
## Detection Rate 0.01034 0.006897 0.006897 0.006897 0.003448
## Detection Prevalence 0.01034 0.006897 0.006897 0.010345 0.003448
## Balanced Accuracy 0.87500 1.000000 1.000000 0.998264 1.000000
## Class: 56 Class: 57 Class: 59 Class: 60 Class: 61
## Sensitivity 1.00000 1.000000 1.000000 1.00000 1.00000
## Specificity 1.00000 1.000000 1.000000 0.99652 1.00000
## Pos Pred Value 1.00000 1.000000 1.000000 0.75000 1.00000
## Neg Pred Value 1.00000 1.000000 1.000000 1.00000 1.00000
## Prevalence 0.01034 0.006897 0.006897 0.01034 0.01724
## Detection Rate 0.01034 0.006897 0.006897 0.01034 0.01724
## Detection Prevalence 0.01034 0.006897 0.006897 0.01379 0.01724
## Balanced Accuracy 1.00000 1.000000 1.000000 0.99826 1.00000
## Class: 62 Class: 63 Class: 64 Class: 65 Class: 66
## Sensitivity 1.00000 1.00000 0.75000 1.00000 1.000000
## Specificity 1.00000 1.00000 0.99650 0.99652 1.000000
## Pos Pred Value 1.00000 1.00000 0.75000 0.75000 1.000000
## Neg Pred Value 1.00000 1.00000 0.99650 1.00000 1.000000
## Prevalence 0.01034 0.01724 0.01379 0.01034 0.003448
## Detection Rate 0.01034 0.01724 0.01034 0.01034 0.003448
## Detection Prevalence 0.01034 0.01724 0.01379 0.01379 0.003448
## Balanced Accuracy 1.00000 1.00000 0.87325 0.99826 1.000000
## Class: 67 Class: 68 Class: 69 Class: 70 Class: 71
## Sensitivity 1.00000 1.000000 0.666667 1.00000 1.00000
## Specificity 1.00000 0.993056 1.000000 1.00000 1.00000
## Pos Pred Value 1.00000 0.500000 1.000000 1.00000 1.00000
## Neg Pred Value 1.00000 1.000000 0.996528 1.00000 1.00000
## Prevalence 0.01724 0.006897 0.010345 0.01034 0.01379
## Detection Rate 0.01724 0.006897 0.006897 0.01034 0.01379
## Detection Prevalence 0.01724 0.013793 0.006897 0.01034 0.01379
## Balanced Accuracy 1.00000 0.996528 0.833333 1.00000 1.00000
## Class: 72 Class: 73 Class: 74 Class: 75 Class: 76
## Sensitivity 1.00000 0.666667 1.00000 1.000000 1.000000
## Specificity 0.99652 1.000000 1.00000 1.000000 1.000000
## Pos Pred Value 0.75000 1.000000 1.00000 1.000000 1.000000
## Neg Pred Value 1.00000 0.996528 1.00000 1.000000 1.000000
## Prevalence 0.01034 0.010345 0.01034 0.003448 0.006897
## Detection Rate 0.01034 0.006897 0.01034 0.003448 0.006897
## Detection Prevalence 0.01379 0.006897 0.01034 0.003448 0.006897
## Balanced Accuracy 0.99826 0.833333 1.00000 1.000000 1.000000
## Class: 77 Class: 78 Class: 79 Class: 80 Class: 81
## Sensitivity 0.75000 1.000000 1.000000 1.000000 1.000000
## Specificity 0.99650 1.000000 1.000000 1.000000 0.996528
## Pos Pred Value 0.75000 1.000000 1.000000 1.000000 0.666667
## Neg Pred Value 0.99650 1.000000 1.000000 1.000000 1.000000
## Prevalence 0.01379 0.006897 0.006897 0.006897 0.006897
## Detection Rate 0.01034 0.006897 0.006897 0.006897 0.006897
## Detection Prevalence 0.01379 0.006897 0.006897 0.006897 0.010345
## Balanced Accuracy 0.87325 1.000000 1.000000 1.000000 0.998264
## Class: 82 Class: 83 Class: 84 Class: 85 Class: 86
## Sensitivity 1.00000 0.666667 0.66667 0.60000 1.00000
## Specificity 1.00000 1.000000 1.00000 1.00000 1.00000
## Pos Pred Value 1.00000 1.000000 1.00000 1.00000 1.00000
## Neg Pred Value 1.00000 0.996528 0.99301 0.99303 1.00000
## Prevalence 0.01034 0.010345 0.02069 0.01724 0.01379
## Detection Rate 0.01034 0.006897 0.01379 0.01034 0.01379
## Detection Prevalence 0.01034 0.006897 0.01379 0.01034 0.01379
## Balanced Accuracy 1.00000 0.833333 0.83333 0.80000 1.00000
## Class: 87 Class: 88 Class: 89 Class: 90 Class: 91
## Sensitivity 1.000000 0.75000 0.666667 1.00000 1.00000
## Specificity 1.000000 1.00000 1.000000 1.00000 1.00000
## Pos Pred Value 1.000000 1.00000 1.000000 1.00000 1.00000
## Neg Pred Value 1.000000 0.99652 0.996528 1.00000 1.00000
## Prevalence 0.006897 0.01379 0.010345 0.01034 0.01034
## Detection Rate 0.006897 0.01034 0.006897 0.01034 0.01034
## Detection Prevalence 0.006897 0.01034 0.006897 0.01034 0.01034
## Balanced Accuracy 1.000000 0.87500 0.833333 1.00000 1.00000
## Class: 92 Class: 93 Class: 94 Class: 95 Class: 96
## Sensitivity 0.60000 1.00000 1.000000 1.000000 1.00000
## Specificity 1.00000 1.00000 0.996528 0.996540 1.00000
## Pos Pred Value 1.00000 1.00000 0.666667 0.500000 1.00000
## Neg Pred Value 0.99303 1.00000 1.000000 1.000000 1.00000
## Prevalence 0.01724 0.01034 0.006897 0.003448 0.01034
## Detection Rate 0.01034 0.01034 0.006897 0.003448 0.01034
## Detection Prevalence 0.01034 0.01034 0.010345 0.006897 0.01034
## Balanced Accuracy 0.80000 1.00000 0.998264 0.998270 1.00000
## Class: 97 Class: 98
## Sensitivity 1.00000 0.60000
## Specificity 1.00000 0.99298
## Pos Pred Value 1.00000 0.60000
## Neg Pred Value 1.00000 0.99298
## Prevalence 0.01034 0.01724
## Detection Rate 0.01034 0.01034
## Detection Prevalence 0.01034 0.01724
## Balanced Accuracy 1.00000 0.79649