library(caret)
## Loading required package: lattice
## Loading required package: ggplot2
library(kernlab)
##
## Attaching package: 'kernlab'
## The following object is masked from 'package:ggplot2':
##
## alpha
data(swiss)
inTrain <- createDataPartition(y = swiss$Fertility, p = 0.80, list = FALSE)
trainingData <- swiss[inTrain,]
testingData <- swiss[-inTrain,]
dim(trainingData)
## [1] 39 6
dim(testingData)
## [1] 8 6
set.seed(13)
model <- train(Fertility ~., data = trainingData, method = "rf")
model
## Random Forest
##
## 39 samples
## 5 predictor
##
## No pre-processing
## Resampling: Bootstrapped (25 reps)
## Summary of sample sizes: 39, 39, 39, 39, 39, 39, ...
## Resampling results across tuning parameters:
##
## mtry RMSE Rsquared MAE
## 2 8.367771 0.6221121 6.739955
## 3 8.419190 0.6059353 6.798356
## 5 8.593480 0.5771038 6.943643
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 2.
prediction <- predict(model, newdata = testingData)
confusionMatrix(
factor(prediction, levels = 1:148),
factor(testingData$Fertility, levels = 1:148)
)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
## 1 0 0 0 0 0 0 0 0 0 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 0 0 0 0 0 0 0 0 0
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## Reference
## Prediction 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
## 1 0 0 0 0 0 0 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 0 0 0 0 0 0
## 3 0 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 0 0 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 0 0 0 0 0 0
## 6 0 0 0 0 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 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 0 0 0 0 0 0
## 9 0 0 0 0 0 0 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 0 0 0 0 0 0
## 11 0 0 0 0 0 0 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 0 0 0 0 0 0
## 13 0 0 0 0 0 0 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 0 0 0 0 0 0
## 15 0 0 0 0 0 0 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 0 0 0 0 0 0
## 17 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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## 111 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 113 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 114 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 115 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 116 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 117 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 118 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 119 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 120 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 121 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 122 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 123 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 124 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 125 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 126 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 127 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 128 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 129 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 130 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 131 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 132 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 133 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 134 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 135 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 136 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 137 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 138 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 139 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 140 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 141 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 142 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 143 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 144 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 145 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 146 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 147 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Reference
## Prediction 139 140 141 142 143 144 145 146 147 148
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0 0 0 0
## 18 0 0 0 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0 0 0 0
## 20 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0
## 24 0 0 0 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 0 0 0 0
## 26 0 0 0 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 0
## 29 0 0 0 0 0 0 0 0 0 0
## 30 0 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0
## 32 0 0 0 0 0 0 0 0 0 0
## 33 0 0 0 0 0 0 0 0 0 0
## 34 0 0 0 0 0 0 0 0 0 0
## 35 0 0 0 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0 0 0 0
## 37 0 0 0 0 0 0 0 0 0 0
## 38 0 0 0 0 0 0 0 0 0 0
## 39 0 0 0 0 0 0 0 0 0 0
## 40 0 0 0 0 0 0 0 0 0 0
## 41 0 0 0 0 0 0 0 0 0 0
## 42 0 0 0 0 0 0 0 0 0 0
## 43 0 0 0 0 0 0 0 0 0 0
## 44 0 0 0 0 0 0 0 0 0 0
## 45 0 0 0 0 0 0 0 0 0 0
## 46 0 0 0 0 0 0 0 0 0 0
## 47 0 0 0 0 0 0 0 0 0 0
## 48 0 0 0 0 0 0 0 0 0 0
## 49 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 0 0
## 51 0 0 0 0 0 0 0 0 0 0
## 52 0 0 0 0 0 0 0 0 0 0
## 53 0 0 0 0 0 0 0 0 0 0
## 54 0 0 0 0 0 0 0 0 0 0
## 55 0 0 0 0 0 0 0 0 0 0
## 56 0 0 0 0 0 0 0 0 0 0
## 57 0 0 0 0 0 0 0 0 0 0
## 58 0 0 0 0 0 0 0 0 0 0
## 59 0 0 0 0 0 0 0 0 0 0
## 60 0 0 0 0 0 0 0 0 0 0
## 61 0 0 0 0 0 0 0 0 0 0
## 62 0 0 0 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 0 0
## 64 0 0 0 0 0 0 0 0 0 0
## 65 0 0 0 0 0 0 0 0 0 0
## 66 0 0 0 0 0 0 0 0 0 0
## 67 0 0 0 0 0 0 0 0 0 0
## 68 0 0 0 0 0 0 0 0 0 0
## 69 0 0 0 0 0 0 0 0 0 0
## 70 0 0 0 0 0 0 0 0 0 0
## 71 0 0 0 0 0 0 0 0 0 0
## 72 0 0 0 0 0 0 0 0 0 0
## 73 0 0 0 0 0 0 0 0 0 0
## 74 0 0 0 0 0 0 0 0 0 0
## 75 0 0 0 0 0 0 0 0 0 0
## 76 0 0 0 0 0 0 0 0 0 0
## 77 0 0 0 0 0 0 0 0 0 0
## 78 0 0 0 0 0 0 0 0 0 0
## 79 0 0 0 0 0 0 0 0 0 0
## 80 0 0 0 0 0 0 0 0 0 0
## 81 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0
## 83 0 0 0 0 0 0 0 0 0 0
## 84 0 0 0 0 0 0 0 0 0 0
## 85 0 0 0 0 0 0 0 0 0 0
## 86 0 0 0 0 0 0 0 0 0 0
## 87 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0 0
## 89 0 0 0 0 0 0 0 0 0 0
## 90 0 0 0 0 0 0 0 0 0 0
## 91 0 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0 0 0 0
## 93 0 0 0 0 0 0 0 0 0 0
## 94 0 0 0 0 0 0 0 0 0 0
## 95 0 0 0 0 0 0 0 0 0 0
## 96 0 0 0 0 0 0 0 0 0 0
## 97 0 0 0 0 0 0 0 0 0 0
## 98 0 0 0 0 0 0 0 0 0 0
## 99 0 0 0 0 0 0 0 0 0 0
## 100 0 0 0 0 0 0 0 0 0 0
## 101 0 0 0 0 0 0 0 0 0 0
## 102 0 0 0 0 0 0 0 0 0 0
## 103 0 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 0 0
## 105 0 0 0 0 0 0 0 0 0 0
## 106 0 0 0 0 0 0 0 0 0 0
## 107 0 0 0 0 0 0 0 0 0 0
## 108 0 0 0 0 0 0 0 0 0 0
## 109 0 0 0 0 0 0 0 0 0 0
## 110 0 0 0 0 0 0 0 0 0 0
## 111 0 0 0 0 0 0 0 0 0 0
## 112 0 0 0 0 0 0 0 0 0 0
## 113 0 0 0 0 0 0 0 0 0 0
## 114 0 0 0 0 0 0 0 0 0 0
## 115 0 0 0 0 0 0 0 0 0 0
## 116 0 0 0 0 0 0 0 0 0 0
## 117 0 0 0 0 0 0 0 0 0 0
## 118 0 0 0 0 0 0 0 0 0 0
## 119 0 0 0 0 0 0 0 0 0 0
## 120 0 0 0 0 0 0 0 0 0 0
## 121 0 0 0 0 0 0 0 0 0 0
## 122 0 0 0 0 0 0 0 0 0 0
## 123 0 0 0 0 0 0 0 0 0 0
## 124 0 0 0 0 0 0 0 0 0 0
## 125 0 0 0 0 0 0 0 0 0 0
## 126 0 0 0 0 0 0 0 0 0 0
## 127 0 0 0 0 0 0 0 0 0 0
## 128 0 0 0 0 0 0 0 0 0 0
## 129 0 0 0 0 0 0 0 0 0 0
## 130 0 0 0 0 0 0 0 0 0 0
## 131 0 0 0 0 0 0 0 0 0 0
## 132 0 0 0 0 0 0 0 0 0 0
## 133 0 0 0 0 0 0 0 0 0 0
## 134 0 0 0 0 0 0 0 0 0 0
## 135 0 0 0 0 0 0 0 0 0 0
## 136 0 0 0 0 0 0 0 0 0 0
## 137 0 0 0 0 0 0 0 0 0 0
## 138 0 0 0 0 0 0 0 0 0 0
## 139 0 0 0 0 0 0 0 0 0 0
## 140 0 0 0 0 0 0 0 0 0 0
## 141 0 0 0 0 0 0 0 0 0 0
## 142 0 0 0 0 0 0 0 0 0 0
## 143 0 0 0 0 0 0 0 0 0 0
## 144 0 0 0 0 0 0 0 0 0 0
## 145 0 0 0 0 0 0 0 0 0 0
## 146 0 0 0 0 0 0 0 0 0 0
## 147 0 0 0 0 0 0 0 0 0 0
## 148 0 0 0 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : NaN
## 95% CI : (NA, NA)
## No Information Rate : NA
## P-Value [Acc > NIR] : NA
##
## Kappa : NaN
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6
## Sensitivity NA NA NA NA NA NA
## Specificity NA NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA NA
## Class: 7 Class: 8 Class: 9 Class: 10 Class: 11
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 12 Class: 13 Class: 14 Class: 15 Class: 16
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 17 Class: 18 Class: 19 Class: 20 Class: 21
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 22 Class: 23 Class: 24 Class: 25 Class: 26
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 27 Class: 28 Class: 29 Class: 30 Class: 31
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 32 Class: 33 Class: 34 Class: 35 Class: 36
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 37 Class: 38 Class: 39 Class: 40 Class: 41
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 42 Class: 43 Class: 44 Class: 45 Class: 46
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 47 Class: 48 Class: 49 Class: 50 Class: 51
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 52 Class: 53 Class: 54 Class: 55 Class: 56
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 57 Class: 58 Class: 59 Class: 60 Class: 61
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 62 Class: 63 Class: 64 Class: 65 Class: 66
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 67 Class: 68 Class: 69 Class: 70 Class: 71
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 72 Class: 73 Class: 74 Class: 75 Class: 76
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 77 Class: 78 Class: 79 Class: 80 Class: 81
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 82 Class: 83 Class: 84 Class: 85 Class: 86
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 87 Class: 88 Class: 89 Class: 90 Class: 91
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 92 Class: 93 Class: 94 Class: 95 Class: 96
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 97 Class: 98 Class: 99 Class: 100 Class: 101
## Sensitivity NA NA NA NA NA
## Specificity NA NA NA NA NA
## Pos Pred Value NA NA NA NA NA
## Neg Pred Value NA NA NA NA NA
## Prevalence NaN NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA NA
## Class: 102 Class: 103 Class: 104 Class: 105
## Sensitivity NA NA NA NA
## Specificity NA NA NA NA
## Pos Pred Value NA NA NA NA
## Neg Pred Value NA NA NA NA
## Prevalence NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA
## Class: 106 Class: 107 Class: 108 Class: 109
## Sensitivity NA NA NA NA
## Specificity NA NA NA NA
## Pos Pred Value NA NA NA NA
## Neg Pred Value NA NA NA NA
## Prevalence NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA
## Class: 110 Class: 111 Class: 112 Class: 113
## Sensitivity NA NA NA NA
## Specificity NA NA NA NA
## Pos Pred Value NA NA NA NA
## Neg Pred Value NA NA NA NA
## Prevalence NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA
## Class: 114 Class: 115 Class: 116 Class: 117
## Sensitivity NA NA NA NA
## Specificity NA NA NA NA
## Pos Pred Value NA NA NA NA
## Neg Pred Value NA NA NA NA
## Prevalence NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA
## Class: 118 Class: 119 Class: 120 Class: 121
## Sensitivity NA NA NA NA
## Specificity NA NA NA NA
## Pos Pred Value NA NA NA NA
## Neg Pred Value NA NA NA NA
## Prevalence NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA
## Class: 122 Class: 123 Class: 124 Class: 125
## Sensitivity NA NA NA NA
## Specificity NA NA NA NA
## Pos Pred Value NA NA NA NA
## Neg Pred Value NA NA NA NA
## Prevalence NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA
## Class: 126 Class: 127 Class: 128 Class: 129
## Sensitivity NA NA NA NA
## Specificity NA NA NA NA
## Pos Pred Value NA NA NA NA
## Neg Pred Value NA NA NA NA
## Prevalence NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA
## Class: 130 Class: 131 Class: 132 Class: 133
## Sensitivity NA NA NA NA
## Specificity NA NA NA NA
## Pos Pred Value NA NA NA NA
## Neg Pred Value NA NA NA NA
## Prevalence NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA
## Class: 134 Class: 135 Class: 136 Class: 137
## Sensitivity NA NA NA NA
## Specificity NA NA NA NA
## Pos Pred Value NA NA NA NA
## Neg Pred Value NA NA NA NA
## Prevalence NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA
## Class: 138 Class: 139 Class: 140 Class: 141
## Sensitivity NA NA NA NA
## Specificity NA NA NA NA
## Pos Pred Value NA NA NA NA
## Neg Pred Value NA NA NA NA
## Prevalence NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA
## Class: 142 Class: 143 Class: 144 Class: 145
## Sensitivity NA NA NA NA
## Specificity NA NA NA NA
## Pos Pred Value NA NA NA NA
## Neg Pred Value NA NA NA NA
## Prevalence NaN NaN NaN NaN
## Detection Rate NaN NaN NaN NaN
## Detection Prevalence NaN NaN NaN NaN
## Balanced Accuracy NA NA NA NA
## Class: 146 Class: 147 Class: 148
## Sensitivity NA NA NA
## Specificity NA NA NA
## Pos Pred Value NA NA NA
## Neg Pred Value NA NA NA
## Prevalence NaN NaN NaN
## Detection Rate NaN NaN NaN
## Detection Prevalence NaN NaN NaN
## Balanced Accuracy NA NA NA
#confusionMatrix(prediction,testingData$Fertility)
plot(model$finalModel)
plot(varImp(model), top = 10)
