Lab Exercise Week 10
S2117893 Min Zhen Tay
1/27/2022
Load the dataset and appropriate packages
library(readxl)
df<-read_xlsx("labW9.xlsx")
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(lattice)
library(caret)
library(klaR)## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectlibrary(MASS)
library(e1071)
library(caTools)Data exploration
str(df)## tibble [768 x 9] (S3: tbl_df/tbl/data.frame)
## $ Pregnancies : num [1:768] 6 1 8 1 0 5 3 10 2 8 ...
## $ Glucose : num [1:768] 148 85 183 89 137 116 78 115 197 125 ...
## $ BloodPressure : num [1:768] 72 66 64 66 40 74 50 0 70 96 ...
## $ SkinThickness : num [1:768] 35 29 0 23 35 0 32 0 45 0 ...
## $ Insulin : num [1:768] 0 0 0 94 168 0 88 0 543 0 ...
## $ BMI : num [1:768] 33.6 26.6 23.3 28.1 43.1 25.6 31 35.3 30.5 0 ...
## $ DiabetesPedigreeFunction: num [1:768] 0.627 0.351 0.672 0.167 2.288 ...
## $ Age : num [1:768] 50 31 32 21 33 30 26 29 53 54 ...
## $ Outcome : num [1:768] 1 0 1 0 1 0 1 0 1 1 ...summary(df)## Pregnancies Glucose BloodPressure SkinThickness
## Min. : 0.000 Min. : 0.0 Min. : 0.00 Min. : 0.00
## 1st Qu.: 1.000 1st Qu.: 99.0 1st Qu.: 62.00 1st Qu.: 0.00
## Median : 3.000 Median :117.0 Median : 72.00 Median :23.00
## Mean : 3.845 Mean :120.9 Mean : 69.11 Mean :20.54
## 3rd Qu.: 6.000 3rd Qu.:140.2 3rd Qu.: 80.00 3rd Qu.:32.00
## Max. :17.000 Max. :199.0 Max. :122.00 Max. :99.00
## Insulin BMI DiabetesPedigreeFunction Age
## Min. : 0.0 Min. : 0.00 Min. :0.0780 Min. :21.00
## 1st Qu.: 0.0 1st Qu.:27.30 1st Qu.:0.2437 1st Qu.:24.00
## Median : 30.5 Median :32.00 Median :0.3725 Median :29.00
## Mean : 79.8 Mean :31.99 Mean :0.4719 Mean :33.24
## 3rd Qu.:127.2 3rd Qu.:36.60 3rd Qu.:0.6262 3rd Qu.:41.00
## Max. :846.0 Max. :67.10 Max. :2.4200 Max. :81.00
## Outcome
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.349
## 3rd Qu.:1.000
## Max. :1.000#The dataset contains 768 entries with 9 features.
#The feature 'Outcome' is the target variable (dependent variable).Checking if data cleaning is necessary
colSums(is.na(df))## Pregnancies Glucose BloodPressure
## 0 0 0
## SkinThickness Insulin BMI
## 0 0 0
## DiabetesPedigreeFunction Age Outcome
## 0 0 0#Since there is no any missing value in each feature, data cleaning is not required.Convert column ‘Outcome’ into factor before proceed to modeling
df$Outcome <- as.factor(df$Outcome) #to ease the modeling process
#Checking the class for 'Outcome'
glimpse(df$Outcome)## Factor w/ 2 levels "0","1": 2 1 2 1 2 1 2 1 2 2 ...Partition data into training and testing set by 70/30
#set random number
set.seed(168)
split = 0.7
trainIndex <- createDataPartition(df$Outcome, p=split, list = FALSE)
data_train <- df[trainIndex, ]
data_test <- df[-trainIndex, ]
#Check the dimension of both training and test subsets
dim(data_train)## [1] 538 9dim(data_test)## [1] 230 9#training set contains 538 entries while testing test contains 230 entriesCheck for cross validation by different methods
#Repeated k fold sample
df_train_control_1<- trainControl(method='repeatedcv', number = 10, repeats = 3)
nbmodel1 <- train(Outcome~., data=data_train, trControl=df_train_control_1, method='nb')
print(nbmodel1)## Naive Bayes
##
## 538 samples
## 8 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 484, 484, 484, 485, 485, 484, ...
## Resampling results across tuning parameters:
##
## usekernel Accuracy Kappa
## FALSE 0.7502562 0.4398002
## TRUE 0.7521663 0.4499119
##
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = TRUE and adjust
## = 1.#Leave one out CV sample
df_train_control_2 <- trainControl(method = 'LOOCV')
nbmodel2 <- train(Outcome~., data=data_train, trControl=df_train_control_2, method='nb')
print(nbmodel2)## Naive Bayes
##
## 538 samples
## 8 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Leave-One-Out Cross-Validation
## Summary of sample sizes: 537, 537, 537, 537, 537, 537, ...
## Resampling results across tuning parameters:
##
## usekernel Accuracy Kappa
## FALSE 0.7490706 0.4391246
## TRUE 0.7546468 0.4576944
##
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = TRUE and adjust
## = 1.#Bootstrap sample
df_train_control_3 <- trainControl(method='boot', number=100)
nbmodel3<- train(Outcome~., data=data_train, trControl=df_train_control_3, method='nb')
print(nbmodel3)## Naive Bayes
##
## 538 samples
## 8 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Bootstrapped (100 reps)
## Summary of sample sizes: 538, 538, 538, 538, 538, 538, ...
## Resampling results across tuning parameters:
##
## usekernel Accuracy Kappa
## FALSE 0.7535646 0.4479311
## TRUE 0.7528664 0.4520643
##
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = FALSE and adjust
## = 1.Train data from training set
model <- NaiveBayes(Outcome~., data=data_train)
model## $apriori
## grouping
## 0 1
## 0.6505576 0.3494424
##
## $tables
## $tables$Pregnancies
## [,1] [,2]
## 0 3.234286 2.955636
## 1 4.755319 3.720693
##
## $tables$Glucose
## [,1] [,2]
## 0 110.9314 26.65724
## 1 140.7660 33.68902
##
## $tables$BloodPressure
## [,1] [,2]
## 0 68.45429 17.69260
## 1 72.38298 20.00754
##
## $tables$SkinThickness
## [,1] [,2]
## 0 19.40571 14.90739
## 1 22.72340 16.68738
##
## $tables$Insulin
## [,1] [,2]
## 0 74.33429 106.4203
## 1 104.64894 144.9853
##
## $tables$BMI
## [,1] [,2]
## 0 30.50429 7.567152
## 1 35.48298 6.668517
##
## $tables$DiabetesPedigreeFunction
## [,1] [,2]
## 0 0.4280171 0.3016743
## 1 0.5776915 0.4038558
##
## $tables$Age
## [,1] [,2]
## 0 30.81429 11.49099
## 1 36.93617 10.61964
##
##
## $levels
## [1] "0" "1"
##
## $call
## NaiveBayes.default(x = X, grouping = Y)
##
## $x
## Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
## 1 6 148 72 35 0 33.6
## 2 1 85 66 29 0 26.6
## 3 8 183 64 0 0 23.3
## 4 1 89 66 23 94 28.1
## 5 0 137 40 35 168 43.1
## 6 5 116 74 0 0 25.6
## 7 3 78 50 32 88 31.0
## 8 10 115 0 0 0 35.3
## 9 2 197 70 45 543 30.5
## 10 4 110 92 0 0 37.6
## 11 10 168 74 0 0 38.0
## 12 10 139 80 0 0 27.1
## 13 1 189 60 23 846 30.1
## 14 5 166 72 19 175 25.8
## 15 7 100 0 0 0 30.0
## 16 0 118 84 47 230 45.8
## 17 1 103 30 38 83 43.3
## 18 1 115 70 30 96 34.6
## 19 3 126 88 41 235 39.3
## 20 8 99 84 0 0 35.4
## 21 7 196 90 0 0 39.8
## 22 9 119 80 35 0 29.0
## 23 10 125 70 26 115 31.1
## 24 7 147 76 0 0 39.4
## 25 13 145 82 19 110 22.2
## 26 5 117 92 0 0 34.1
## 27 5 109 75 26 0 36.0
## 28 3 88 58 11 54 24.8
## 29 6 92 92 0 0 19.9
## 30 10 122 78 31 0 27.6
## 31 4 103 60 33 192 24.0
## 32 11 138 76 0 0 33.2
## 33 9 102 76 37 0 32.9
## 34 2 90 68 42 0 38.2
## 35 4 111 72 47 207 37.1
## 36 3 180 64 25 70 34.0
## 37 7 106 92 18 0 22.7
## 38 7 159 64 0 0 27.4
## 39 0 180 66 39 0 42.0
## 40 1 146 56 0 0 29.7
## 41 7 103 66 32 0 39.1
## 42 7 150 66 42 342 34.7
## 43 1 73 50 10 0 23.0
## 44 0 100 88 60 110 46.8
## 45 0 146 82 0 0 40.5
## 46 0 105 64 41 142 41.5
## 47 2 84 0 0 0 0.0
## 48 8 133 72 0 0 32.9
## 49 5 44 62 0 0 25.0
## 50 0 109 88 30 0 32.5
## 51 2 109 92 0 0 42.7
## 52 13 126 90 0 0 43.4
## 53 4 129 86 20 270 35.1
## 54 1 79 75 30 0 32.0
## 55 1 0 48 20 0 24.7
## 56 7 62 78 0 0 32.6
## 57 0 131 0 0 0 43.2
## 58 2 112 66 22 0 25.0
## 59 3 113 44 13 0 22.4
## 60 2 74 0 0 0 0.0
## 61 7 83 78 26 71 29.3
## 62 0 101 65 28 0 24.6
## 63 2 110 74 29 125 32.4
## 64 13 106 72 54 0 36.6
## 65 15 136 70 32 110 37.1
## 66 1 107 68 19 0 26.5
## 67 1 80 55 0 0 19.1
## 68 4 123 80 15 176 32.0
## 69 2 142 82 18 64 24.7
## 70 6 144 72 27 228 33.9
## 71 2 92 62 28 0 31.6
## 72 6 93 50 30 64 28.7
## 73 1 163 72 0 0 39.0
## 74 1 151 60 0 0 26.1
## 75 0 125 96 0 0 22.5
## 76 1 96 122 0 0 22.4
## 77 4 144 58 28 140 29.5
## 78 3 83 58 31 18 34.3
## 79 0 95 85 25 36 37.4
## 80 3 171 72 33 135 33.3
## 81 8 155 62 26 495 34.0
## 82 7 160 54 32 175 30.5
## 83 4 146 92 0 0 31.2
## 84 5 124 74 0 0 34.0
## 85 5 78 48 0 0 33.7
## 86 4 99 76 15 51 23.2
## 87 6 111 64 39 0 34.2
## 88 2 107 74 30 100 33.6
## 89 5 132 80 0 0 26.8
## 90 0 113 76 0 0 33.3
## 91 3 120 70 30 135 42.9
## 92 1 118 58 36 94 33.3
## 93 1 117 88 24 145 34.5
## 94 4 173 70 14 168 29.7
## 95 9 122 56 0 0 33.3
## 96 3 170 64 37 225 34.5
## 97 8 84 74 31 0 38.3
## 98 2 96 68 13 49 21.1
## 99 0 100 70 26 50 30.8
## 100 0 93 60 25 92 28.7
## 101 0 129 80 0 0 31.2
## 102 5 105 72 29 325 36.9
## 103 3 128 78 0 0 21.1
## 104 5 106 82 30 0 39.5
## 105 2 108 52 26 63 32.5
## 106 4 154 62 31 284 32.8
## 107 0 102 75 23 0 0.0
## 108 5 147 78 0 0 33.7
## 109 2 90 70 17 0 27.3
## 110 1 136 74 50 204 37.4
## 111 4 114 65 0 0 21.9
## 112 9 156 86 28 155 34.3
## 113 1 153 82 42 485 40.6
## 114 7 152 88 44 0 50.0
## 115 2 99 52 15 94 24.6
## 116 1 109 56 21 135 25.2
## 117 2 88 74 19 53 29.0
## 118 7 102 74 40 105 37.2
## 119 0 114 80 34 285 44.2
## 120 0 131 88 0 0 31.6
## 121 6 104 74 18 156 29.9
## 122 4 120 68 0 0 29.6
## 123 4 110 66 0 0 31.9
## 124 3 111 90 12 78 28.4
## 125 6 134 70 23 130 35.4
## 126 2 87 0 23 0 28.9
## 127 1 79 60 42 48 43.5
## 128 6 85 78 0 0 31.2
## 129 0 129 110 46 130 67.1
## 130 5 130 82 0 0 39.1
## 131 1 0 74 20 23 27.7
## 132 4 141 74 0 0 27.6
## 133 7 194 68 28 0 35.9
## 134 8 181 68 36 495 30.1
## 135 1 128 98 41 58 32.0
## 136 5 139 80 35 160 31.6
## 137 3 111 62 0 0 22.6
## 138 9 123 70 44 94 33.1
## 139 7 159 66 0 0 30.4
## 140 11 135 0 0 0 52.3
## 141 8 85 55 20 0 24.4
## 142 5 158 84 41 210 39.4
## 143 1 105 58 0 0 24.3
## 144 3 107 62 13 48 22.9
## 145 4 148 60 27 318 30.9
## 146 0 113 80 16 0 31.0
## 147 5 111 72 28 0 23.9
## 148 8 196 76 29 280 37.5
## 149 5 162 104 0 0 37.7
## 150 1 96 64 27 87 33.2
## 151 7 184 84 33 0 35.5
## 152 2 81 60 22 0 27.7
## 153 0 147 85 54 0 42.8
## 154 7 179 95 31 0 34.2
## 155 0 140 65 26 130 42.6
## 156 9 112 82 32 175 34.2
## 157 5 109 62 41 129 35.8
## 158 6 125 68 30 120 30.0
## 159 5 85 74 22 0 29.0
## 160 2 158 90 0 0 31.6
## 161 7 119 0 0 0 25.2
## 162 1 100 66 15 56 23.6
## 163 1 87 78 27 32 34.6
## 164 0 101 76 0 0 35.7
## 165 4 197 70 39 744 36.7
## 166 0 117 80 31 53 45.2
## 167 6 134 80 37 370 46.2
## 168 1 79 80 25 37 25.4
## 169 3 74 68 28 45 29.7
## 170 7 181 84 21 192 35.9
## 171 9 164 84 21 0 30.8
## 172 1 91 64 24 0 29.2
## 173 4 91 70 32 88 33.1
## 174 3 139 54 0 0 25.6
## 175 6 119 50 22 176 27.1
## 176 9 184 85 15 0 30.0
## 177 0 165 90 33 680 52.3
## 178 9 124 70 33 402 35.4
## 179 2 90 80 14 55 24.4
## 180 12 92 62 7 258 27.6
## 181 3 111 56 39 0 30.1
## 182 2 114 68 22 0 28.7
## 183 11 155 76 28 150 33.3
## 184 3 191 68 15 130 30.9
## 185 3 141 0 0 0 30.0
## 186 4 95 70 32 0 32.1
## 187 4 123 62 0 0 32.0
## 188 0 138 0 0 0 36.3
## 189 2 128 64 42 0 40.0
## 190 0 102 52 0 0 25.1
## 191 10 101 86 37 0 45.6
## 192 3 122 78 0 0 23.0
## 193 1 71 78 50 45 33.2
## 194 7 106 60 24 0 26.5
## 195 2 108 62 10 278 25.3
## 196 0 146 70 0 0 37.9
## 197 10 129 76 28 122 35.9
## 198 7 133 88 15 155 32.4
## 199 2 108 80 0 0 27.0
## 200 7 136 74 26 135 26.0
## 201 5 155 84 44 545 38.7
## 202 1 119 86 39 220 45.6
## 203 0 78 88 29 40 36.9
## 204 0 107 62 30 74 36.6
## 205 2 128 78 37 182 43.3
## 206 0 161 50 0 0 21.9
## 207 6 151 62 31 120 35.5
## 208 14 100 78 25 184 36.6
## 209 8 112 72 0 0 23.6
## 210 2 144 58 33 135 31.6
## 211 5 77 82 41 42 35.8
## 212 5 115 98 0 0 52.9
## 213 3 150 76 0 0 21.0
## 214 2 120 76 37 105 39.7
## 215 10 161 68 23 132 25.5
## 216 0 137 68 14 148 24.8
## 217 2 155 74 17 96 26.6
## 218 3 113 50 10 85 29.5
## 219 7 109 80 31 0 35.9
## 220 3 99 80 11 64 19.3
## 221 3 182 74 0 0 30.5
## 222 6 194 78 0 0 23.5
## 223 4 129 60 12 231 27.5
## 224 3 112 74 30 0 31.6
## 225 0 124 70 20 0 27.4
## 226 13 152 90 33 29 26.8
## 227 2 112 75 32 0 35.7
## 228 1 157 72 21 168 25.6
## 229 1 122 64 32 156 35.1
## 230 10 179 70 0 0 35.1
## 231 2 102 86 36 120 45.5
## 232 6 105 70 32 68 30.8
## 233 8 118 72 19 0 23.1
## 234 2 87 58 16 52 32.7
## 235 1 180 0 0 0 43.3
## 236 1 95 60 18 58 23.9
## 237 0 165 76 43 255 47.9
## 238 0 117 0 0 0 33.8
## 239 5 115 76 0 0 31.2
## 240 9 152 78 34 171 34.2
## 241 7 178 84 0 0 39.9
## 242 1 130 70 13 105 25.9
## 243 1 95 74 21 73 25.9
## 244 1 139 46 19 83 28.7
## 245 3 99 62 19 74 21.8
## 246 5 0 80 32 0 41.0
## 247 4 92 80 0 0 42.2
## 248 4 137 84 0 0 31.2
## 249 3 61 82 28 0 34.4
## 250 1 90 62 12 43 27.2
## 251 3 90 78 0 0 42.7
## 252 1 125 50 40 167 33.3
## 253 12 88 74 40 54 35.3
## 254 1 196 76 36 249 36.5
## 255 5 189 64 33 325 31.2
## 256 5 103 108 37 0 39.2
## 257 4 147 74 25 293 34.9
## 258 6 124 72 0 0 27.6
## 259 0 101 64 17 0 21.0
## 260 3 81 86 16 66 27.5
## 261 1 133 102 28 140 32.8
## 262 3 173 82 48 465 38.4
## 263 0 84 64 22 66 35.8
## 264 2 105 58 40 94 34.9
## 265 2 122 52 43 158 36.2
## 266 12 140 82 43 325 39.2
## 267 0 98 82 15 84 25.2
## 268 1 87 60 37 75 37.2
## 269 0 93 100 39 72 43.4
## 270 1 107 72 30 82 30.8
## 271 0 105 68 22 0 20.0
## 272 1 109 60 8 182 25.4
## 273 1 90 62 18 59 25.1
## 274 1 119 54 13 50 22.3
## 275 5 116 74 29 0 32.3
## 276 8 105 100 36 0 43.3
## 277 5 144 82 26 285 32.0
## 278 5 166 76 0 0 45.7
## 279 4 116 72 12 87 22.1
## 280 4 158 78 0 0 32.9
## 281 2 127 58 24 275 27.7
## 282 3 96 56 34 115 24.7
## 283 0 131 66 40 0 34.3
## 284 3 82 70 0 0 21.1
## 285 3 193 70 31 0 34.9
## 286 4 95 64 0 0 32.0
## 287 5 136 84 41 88 35.0
## 288 9 72 78 25 0 31.6
## 289 2 123 48 32 165 42.1
## 290 8 197 74 0 0 25.9
## 291 1 172 68 49 579 42.4
## 292 1 112 72 30 176 34.4
## 293 1 143 84 23 310 42.4
## 294 3 173 84 33 474 35.7
## 295 1 97 68 21 0 27.2
## 296 4 144 82 32 0 38.5
## 297 3 129 64 29 115 26.4
## 298 1 119 88 41 170 45.3
## 299 2 94 68 18 76 26.0
## 300 0 102 64 46 78 40.6
## 301 8 151 78 32 210 42.9
## 302 4 184 78 39 277 37.0
## 303 1 181 64 30 180 34.1
## 304 0 135 94 46 145 40.6
## 305 1 95 82 25 180 35.0
## 306 2 99 0 0 0 22.2
## 307 3 89 74 16 85 30.4
## 308 1 80 74 11 60 30.0
## 309 2 139 75 0 0 25.6
## 310 5 147 75 0 0 29.9
## 311 6 107 88 0 0 36.8
## 312 0 189 104 25 0 34.3
## 313 4 117 64 27 120 33.2
## 314 8 108 70 0 0 30.5
## 315 4 117 62 12 0 29.7
## 316 0 180 78 63 14 59.4
## 317 0 120 74 18 63 30.5
## 318 1 82 64 13 95 21.2
## 319 2 134 70 0 0 28.9
## 320 0 91 68 32 210 39.9
## 321 2 100 54 28 105 37.8
## 322 1 135 54 0 0 26.7
## 323 1 71 62 0 0 21.8
## 324 8 74 70 40 49 35.3
## 325 5 88 78 30 0 27.6
## 326 0 124 56 13 105 21.8
## 327 0 97 64 36 100 36.8
## 328 8 120 0 0 0 30.0
## 329 6 154 78 41 140 46.1
## 330 0 137 70 38 0 33.2
## 331 0 119 66 27 0 38.8
## 332 7 136 90 0 0 29.9
## 333 4 114 64 0 0 28.9
## 334 0 137 84 27 0 27.3
## 335 4 132 86 31 0 28.0
## 336 3 158 70 30 328 35.5
## 337 0 123 88 37 0 35.2
## 338 4 85 58 22 49 27.8
## 339 0 84 82 31 125 38.2
## 340 0 145 0 0 0 44.2
## 341 0 135 68 42 250 42.3
## 342 1 139 62 41 480 40.7
## 343 0 173 78 32 265 46.5
## 344 4 99 72 17 0 25.6
## 345 8 194 80 0 0 26.1
## 346 2 83 65 28 66 36.8
## 347 4 99 68 38 0 32.8
## 348 4 125 70 18 122 28.9
## 349 3 80 0 0 0 0.0
## 350 6 166 74 0 0 26.6
## 351 5 110 68 0 0 26.0
## 352 2 81 72 15 76 30.1
## 353 7 195 70 33 145 25.1
## 354 6 154 74 32 193 29.3
## 355 2 117 90 19 71 25.2
## 356 3 84 72 32 0 37.2
## 357 6 0 68 41 0 39.0
## 358 7 94 64 25 79 33.3
## 359 2 84 50 23 76 30.4
## 360 8 120 78 0 0 25.0
## 361 12 84 72 31 0 29.7
## 362 0 139 62 17 210 22.1
## 363 9 91 68 0 0 24.2
## 364 7 125 86 0 0 37.6
## 365 13 76 60 0 0 32.8
## 366 6 129 90 7 326 19.6
## 367 3 124 80 33 130 33.2
## 368 6 114 0 0 0 0.0
## 369 9 130 70 0 0 34.2
## 370 3 125 58 0 0 31.6
## 371 3 87 60 18 0 21.8
## 372 1 97 64 19 82 18.2
## 373 3 116 74 15 105 26.3
## 374 0 117 66 31 188 30.8
## 375 0 111 65 0 0 24.6
## 376 2 122 60 18 106 29.8
## 377 0 107 76 0 0 45.3
## 378 1 77 56 30 56 33.3
## 379 0 57 60 0 0 21.7
## 380 0 127 80 37 210 36.3
## 381 3 129 92 49 155 36.4
## 382 10 90 85 32 0 34.9
## 383 4 84 90 23 56 39.5
## 384 1 88 78 29 76 32.0
## 385 5 187 76 27 207 43.6
## 386 4 131 68 21 166 33.1
## 387 1 164 82 43 67 32.8
## 388 1 88 62 24 44 29.9
## 389 1 84 64 23 115 36.9
## 390 7 124 70 33 215 25.5
## 391 11 85 74 0 0 30.1
## 392 6 125 76 0 0 33.8
## 393 0 198 66 32 274 41.3
## 394 6 99 60 19 54 26.9
## 395 2 95 54 14 88 26.1
## 396 6 92 62 32 126 32.0
## 397 4 154 72 29 126 31.3
## 398 0 121 66 30 165 34.3
## 399 2 130 96 0 0 22.6
## 400 3 111 58 31 44 29.5
## 401 2 98 60 17 120 34.7
## 402 6 108 44 20 130 24.0
## 403 2 118 80 0 0 42.9
## 404 10 133 68 0 0 27.0
## 405 0 151 90 46 0 42.1
## 406 6 109 60 27 0 25.0
## 407 12 121 78 17 0 26.5
## 408 8 100 76 0 0 38.7
## 409 8 124 76 24 600 28.7
## 410 1 93 56 11 0 22.5
## 411 6 103 66 0 0 24.3
## 412 3 176 86 27 156 33.3
## 413 0 73 0 0 0 21.1
## 414 11 111 84 40 0 46.8
## 415 2 112 78 50 140 39.4
## 416 6 123 72 45 230 33.6
## 417 0 188 82 14 185 32.0
## 418 0 67 76 0 0 45.3
## 419 1 173 74 0 0 36.8
## 420 1 109 38 18 120 23.1
## 421 1 108 88 19 0 27.1
## 422 6 96 0 0 0 23.7
## 423 7 150 78 29 126 35.2
## 424 1 124 60 32 0 35.8
## 425 1 181 78 42 293 40.0
## 426 1 92 62 25 41 19.5
## 427 0 152 82 39 272 41.5
## 428 3 106 54 21 158 30.9
## 429 7 168 88 42 321 38.2
## 430 11 138 74 26 144 36.1
## 431 3 106 72 0 0 25.8
## 432 6 117 96 0 0 28.7
## 433 2 68 62 13 15 20.1
## 434 9 112 82 24 0 28.2
## 435 0 119 0 0 0 32.4
## 436 2 112 86 42 160 38.4
## 437 2 92 76 20 0 24.2
## 438 6 183 94 0 0 40.8
## 439 2 108 64 0 0 30.8
## 440 4 90 88 47 54 37.7
## 441 0 125 68 0 0 24.7
## 442 0 132 78 0 0 32.4
## 443 5 128 80 0 0 34.6
## 444 2 111 60 0 0 26.2
## 445 1 128 82 17 183 27.5
## 446 10 92 62 0 0 25.9
## 447 13 104 72 0 0 31.2
## 448 5 104 74 0 0 28.8
## 449 2 94 76 18 66 31.6
## 450 0 102 86 17 105 29.3
## 451 6 147 80 0 0 29.5
## 452 3 103 72 30 152 27.6
## 453 2 157 74 35 440 39.4
## 454 1 167 74 17 144 23.4
## 455 0 179 50 36 159 37.8
## 456 11 136 84 35 130 28.3
## 457 5 123 74 40 77 34.1
## 458 2 120 54 0 0 26.8
## 459 1 106 70 28 135 34.2
## 460 2 101 58 35 90 21.8
## 461 1 120 80 48 200 38.9
## 462 11 127 106 0 0 39.0
## 463 3 80 82 31 70 34.2
## 464 10 162 84 0 0 27.7
## 465 1 199 76 43 0 42.9
## 466 8 167 106 46 231 37.6
## 467 6 115 60 39 0 33.7
## 468 6 165 68 26 168 33.6
## 469 1 99 58 10 0 25.4
## 470 3 123 100 35 240 57.3
## 471 8 91 82 0 0 35.6
## 472 6 195 70 0 0 30.9
## 473 9 156 86 0 0 24.8
## 474 0 93 60 0 0 35.3
## 475 3 121 52 0 0 36.0
## 476 2 101 58 17 265 24.2
## 477 2 56 56 28 45 24.2
## 478 0 162 76 36 0 49.6
## 479 0 95 64 39 105 44.6
## 480 4 125 80 0 0 32.3
## 481 3 130 64 0 0 23.1
## 482 1 107 50 19 0 28.3
## 483 1 140 74 26 180 24.1
## 484 1 144 82 46 180 46.1
## 485 13 158 114 0 0 42.3
## 486 2 121 70 32 95 39.1
## 487 7 129 68 49 125 38.5
## 488 7 142 90 24 480 30.4
## 489 0 99 0 0 0 25.0
## 490 4 127 88 11 155 34.5
## 491 4 118 70 0 0 44.5
## 492 2 122 76 27 200 35.9
## 493 6 125 78 31 0 27.6
## 494 1 168 88 29 0 35.0
## 495 4 110 76 20 100 28.4
## 496 2 127 46 21 335 34.4
## 497 2 93 64 32 160 38.0
## 498 3 158 64 13 387 31.2
## 499 5 126 78 27 22 29.6
## 500 10 129 62 36 0 41.2
## 501 3 102 74 0 0 29.5
## 502 3 173 78 39 185 33.8
## 503 10 94 72 18 0 23.1
## 504 1 108 60 46 178 35.5
## 505 4 83 86 19 0 29.3
## 506 1 114 66 36 200 38.1
## 507 5 117 86 30 105 39.1
## 508 1 116 78 29 180 36.1
## 509 0 141 84 26 0 32.4
## 510 2 175 88 0 0 22.9
## 511 2 92 52 0 0 30.1
## 512 2 174 88 37 120 44.5
## 513 2 105 75 0 0 23.3
## 514 4 95 60 32 0 35.4
## 515 0 126 86 27 120 27.4
## 516 8 65 72 23 0 32.0
## 517 2 99 60 17 160 36.6
## 518 1 102 74 0 0 39.5
## 519 11 120 80 37 150 42.3
## 520 3 102 44 20 94 30.8
## 521 1 109 58 18 116 28.5
## 522 9 140 94 0 0 32.7
## 523 12 100 84 33 105 30.0
## 524 1 147 94 41 0 49.3
## 525 3 187 70 22 200 36.4
## 526 1 121 78 39 74 39.0
## 527 3 108 62 24 0 26.0
## 528 0 181 88 44 510 43.3
## 529 1 128 88 39 110 36.5
## 530 0 123 72 0 0 36.3
## 531 1 106 76 0 0 37.5
## 532 6 190 92 0 0 35.5
## 533 2 88 58 26 16 28.4
## 534 9 89 62 0 0 22.5
## 535 10 101 76 48 180 32.9
## 536 2 122 70 27 0 36.8
## 537 5 121 72 23 112 26.2
## 538 1 93 70 31 0 30.4
## DiabetesPedigreeFunction Age
## 1 0.627 50
## 2 0.351 31
## 3 0.672 32
## 4 0.167 21
## 5 2.288 33
## 6 0.201 30
## 7 0.248 26
## 8 0.134 29
## 9 0.158 53
## 10 0.191 30
## 11 0.537 34
## 12 1.441 57
## 13 0.398 59
## 14 0.587 51
## 15 0.484 32
## 16 0.551 31
## 17 0.183 33
## 18 0.529 32
## 19 0.704 27
## 20 0.388 50
## 21 0.451 41
## 22 0.263 29
## 23 0.205 41
## 24 0.257 43
## 25 0.245 57
## 26 0.337 38
## 27 0.546 60
## 28 0.267 22
## 29 0.188 28
## 30 0.512 45
## 31 0.966 33
## 32 0.420 35
## 33 0.665 46
## 34 0.503 27
## 35 1.390 56
## 36 0.271 26
## 37 0.235 48
## 38 0.294 40
## 39 1.893 25
## 40 0.564 29
## 41 0.344 31
## 42 0.718 42
## 43 0.248 21
## 44 0.962 31
## 45 1.781 44
## 46 0.173 22
## 47 0.304 21
## 48 0.270 39
## 49 0.587 36
## 50 0.855 38
## 51 0.845 54
## 52 0.583 42
## 53 0.231 23
## 54 0.396 22
## 55 0.140 22
## 56 0.391 41
## 57 0.270 26
## 58 0.307 24
## 59 0.140 22
## 60 0.102 22
## 61 0.767 36
## 62 0.237 22
## 63 0.698 27
## 64 0.178 45
## 65 0.153 43
## 66 0.165 24
## 67 0.258 21
## 68 0.443 34
## 69 0.761 21
## 70 0.255 40
## 71 0.130 24
## 72 0.356 23
## 73 1.222 33
## 74 0.179 22
## 75 0.262 21
## 76 0.207 27
## 77 0.287 37
## 78 0.336 25
## 79 0.247 24
## 80 0.199 24
## 81 0.543 46
## 82 0.588 39
## 83 0.539 61
## 84 0.220 38
## 85 0.654 25
## 86 0.223 21
## 87 0.260 24
## 88 0.404 23
## 89 0.186 69
## 90 0.278 23
## 91 0.452 30
## 92 0.261 23
## 93 0.403 40
## 94 0.361 33
## 95 1.114 33
## 96 0.356 30
## 97 0.457 39
## 98 0.647 26
## 99 0.597 21
## 100 0.532 22
## 101 0.703 29
## 102 0.159 28
## 103 0.268 55
## 104 0.286 38
## 105 0.318 22
## 106 0.237 23
## 107 0.572 21
## 108 0.218 65
## 109 0.085 22
## 110 0.399 24
## 111 0.432 37
## 112 1.189 42
## 113 0.687 23
## 114 0.337 36
## 115 0.637 21
## 116 0.833 23
## 117 0.229 22
## 118 0.204 45
## 119 0.167 27
## 120 0.743 32
## 121 0.722 41
## 122 0.709 34
## 123 0.471 29
## 124 0.495 29
## 125 0.542 29
## 126 0.773 25
## 127 0.678 23
## 128 0.382 42
## 129 0.319 26
## 130 0.956 37
## 131 0.299 21
## 132 0.244 40
## 133 0.745 41
## 134 0.615 60
## 135 1.321 33
## 136 0.361 25
## 137 0.142 21
## 138 0.374 40
## 139 0.383 36
## 140 0.578 40
## 141 0.136 42
## 142 0.395 29
## 143 0.187 21
## 144 0.678 23
## 145 0.150 29
## 146 0.874 21
## 147 0.407 27
## 148 0.605 57
## 149 0.151 52
## 150 0.289 21
## 151 0.355 41
## 152 0.290 25
## 153 0.375 24
## 154 0.164 60
## 155 0.431 24
## 156 0.260 36
## 157 0.514 25
## 158 0.464 32
## 159 1.224 32
## 160 0.805 66
## 161 0.209 37
## 162 0.666 26
## 163 0.101 22
## 164 0.198 26
## 165 2.329 31
## 166 0.089 24
## 167 0.238 46
## 168 0.583 22
## 169 0.293 23
## 170 0.586 51
## 171 0.831 32
## 172 0.192 21
## 173 0.446 22
## 174 0.402 22
## 175 1.318 33
## 176 1.213 49
## 177 0.427 23
## 178 0.282 34
## 179 0.249 24
## 180 0.926 44
## 181 0.557 30
## 182 0.092 25
## 183 1.353 51
## 184 0.299 34
## 185 0.761 27
## 186 0.612 24
## 187 0.226 35
## 188 0.933 25
## 189 1.101 24
## 190 0.078 21
## 191 1.136 38
## 192 0.254 40
## 193 0.422 21
## 194 0.296 29
## 195 0.881 22
## 196 0.334 28
## 197 0.280 39
## 198 0.262 37
## 199 0.259 52
## 200 0.647 51
## 201 0.619 34
## 202 0.808 29
## 203 0.434 21
## 204 0.757 25
## 205 1.224 31
## 206 0.254 65
## 207 0.692 28
## 208 0.412 46
## 209 0.840 58
## 210 0.422 25
## 211 0.156 35
## 212 0.209 28
## 213 0.207 37
## 214 0.215 29
## 215 0.326 47
## 216 0.143 21
## 217 0.433 27
## 218 0.626 25
## 219 1.127 43
## 220 0.284 30
## 221 0.345 29
## 222 0.129 59
## 223 0.527 31
## 224 0.197 25
## 225 0.254 36
## 226 0.731 43
## 227 0.148 21
## 228 0.123 24
## 229 0.692 30
## 230 0.200 37
## 231 0.127 23
## 232 0.122 37
## 233 1.476 46
## 234 0.166 25
## 235 0.282 41
## 236 0.260 22
## 237 0.259 26
## 238 0.932 44
## 239 0.343 44
## 240 0.893 33
## 241 0.331 41
## 242 0.472 22
## 243 0.673 36
## 244 0.654 22
## 245 0.279 26
## 246 0.346 37
## 247 0.237 29
## 248 0.252 30
## 249 0.243 46
## 250 0.580 24
## 251 0.559 21
## 252 0.962 28
## 253 0.378 48
## 254 0.875 29
## 255 0.583 29
## 256 0.305 65
## 257 0.385 30
## 258 0.368 29
## 259 0.252 21
## 260 0.306 22
## 261 0.234 45
## 262 2.137 25
## 263 0.545 21
## 264 0.225 25
## 265 0.816 28
## 266 0.528 58
## 267 0.299 22
## 268 0.509 22
## 269 1.021 35
## 270 0.821 24
## 271 0.236 22
## 272 0.947 21
## 273 1.268 25
## 274 0.205 24
## 275 0.660 35
## 276 0.239 45
## 277 0.452 58
## 278 0.340 27
## 279 0.463 37
## 280 0.803 31
## 281 1.600 25
## 282 0.944 39
## 283 0.196 22
## 284 0.389 25
## 285 0.241 25
## 286 0.161 31
## 287 0.286 35
## 288 0.280 38
## 289 0.520 26
## 290 1.191 39
## 291 0.702 28
## 292 0.528 25
## 293 1.076 22
## 294 0.258 22
## 295 1.095 22
## 296 0.554 37
## 297 0.219 28
## 298 0.507 26
## 299 0.561 21
## 300 0.496 21
## 301 0.516 36
## 302 0.264 31
## 303 0.328 38
## 304 0.284 26
## 305 0.233 43
## 306 0.108 23
## 307 0.551 38
## 308 0.527 22
## 309 0.167 29
## 310 0.434 28
## 311 0.727 31
## 312 0.435 41
## 313 0.230 24
## 314 0.955 33
## 315 0.380 30
## 316 2.420 25
## 317 0.285 26
## 318 0.415 23
## 319 0.542 23
## 320 0.381 25
## 321 0.498 24
## 322 0.687 62
## 323 0.416 26
## 324 0.705 39
## 325 0.258 37
## 326 0.452 21
## 327 0.600 25
## 328 0.183 38
## 329 0.571 27
## 330 0.170 22
## 331 0.259 22
## 332 0.210 50
## 333 0.126 24
## 334 0.231 59
## 335 0.419 63
## 336 0.344 35
## 337 0.197 29
## 338 0.306 28
## 339 0.233 23
## 340 0.630 31
## 341 0.365 24
## 342 0.536 21
## 343 1.159 58
## 344 0.294 28
## 345 0.551 67
## 346 0.629 24
## 347 0.145 33
## 348 1.144 45
## 349 0.174 22
## 350 0.304 66
## 351 0.292 30
## 352 0.547 25
## 353 0.163 55
## 354 0.839 39
## 355 0.313 21
## 356 0.267 28
## 357 0.727 41
## 358 0.738 41
## 359 0.968 21
## 360 0.409 64
## 361 0.297 46
## 362 0.207 21
## 363 0.200 58
## 364 0.304 51
## 365 0.180 41
## 366 0.582 60
## 367 0.305 26
## 368 0.189 26
## 369 0.652 45
## 370 0.151 24
## 371 0.444 21
## 372 0.299 21
## 373 0.107 24
## 374 0.493 22
## 375 0.660 31
## 376 0.717 22
## 377 0.686 24
## 378 1.251 24
## 379 0.735 67
## 380 0.804 23
## 381 0.968 32
## 382 0.825 56
## 383 0.159 25
## 384 0.365 29
## 385 1.034 53
## 386 0.160 28
## 387 0.341 50
## 388 0.422 23
## 389 0.471 28
## 390 0.161 37
## 391 0.300 35
## 392 0.121 54
## 393 0.502 28
## 394 0.497 32
## 395 0.748 22
## 396 0.085 46
## 397 0.338 37
## 398 0.203 33
## 399 0.268 21
## 400 0.430 22
## 401 0.198 22
## 402 0.813 35
## 403 0.693 21
## 404 0.245 36
## 405 0.371 21
## 406 0.206 27
## 407 0.259 62
## 408 0.190 42
## 409 0.687 52
## 410 0.417 22
## 411 0.249 29
## 412 1.154 52
## 413 0.342 25
## 414 0.925 45
## 415 0.175 24
## 416 0.733 34
## 417 0.682 22
## 418 0.194 46
## 419 0.088 38
## 420 0.407 26
## 421 0.400 24
## 422 0.190 28
## 423 0.692 54
## 424 0.514 21
## 425 1.258 22
## 426 0.482 25
## 427 0.270 27
## 428 0.292 24
## 429 0.787 40
## 430 0.557 50
## 431 0.207 27
## 432 0.157 30
## 433 0.257 23
## 434 1.282 50
## 435 0.141 24
## 436 0.246 28
## 437 1.698 28
## 438 1.461 45
## 439 0.158 21
## 440 0.362 29
## 441 0.206 21
## 442 0.393 21
## 443 0.144 45
## 444 0.343 23
## 445 0.115 22
## 446 0.167 31
## 447 0.465 38
## 448 0.153 48
## 449 0.649 23
## 450 0.695 27
## 451 0.178 50
## 452 0.730 27
## 453 0.134 30
## 454 0.447 33
## 455 0.455 22
## 456 0.260 42
## 457 0.269 28
## 458 0.455 27
## 459 0.142 22
## 460 0.155 22
## 461 1.162 41
## 462 0.190 51
## 463 1.292 27
## 464 0.182 54
## 465 1.394 22
## 466 0.165 43
## 467 0.245 40
## 468 0.631 49
## 469 0.551 21
## 470 0.880 22
## 471 0.587 68
## 472 0.328 31
## 473 0.230 53
## 474 0.263 25
## 475 0.127 25
## 476 0.614 23
## 477 0.332 22
## 478 0.364 26
## 479 0.366 22
## 480 0.536 27
## 481 0.314 22
## 482 0.181 29
## 483 0.828 23
## 484 0.335 46
## 485 0.257 44
## 486 0.886 23
## 487 0.439 43
## 488 0.128 43
## 489 0.253 22
## 490 0.598 28
## 491 0.904 26
## 492 0.483 26
## 493 0.565 49
## 494 0.905 52
## 495 0.118 27
## 496 0.176 22
## 497 0.674 23
## 498 0.295 24
## 499 0.439 40
## 500 0.441 38
## 501 0.121 32
## 502 0.970 31
## 503 0.595 56
## 504 0.415 24
## 505 0.317 34
## 506 0.289 21
## 507 0.251 42
## 508 0.496 25
## 509 0.433 22
## 510 0.326 22
## 511 0.141 22
## 512 0.646 24
## 513 0.560 53
## 514 0.284 28
## 515 0.515 21
## 516 0.600 42
## 517 0.453 21
## 518 0.293 42
## 519 0.785 48
## 520 0.400 26
## 521 0.219 22
## 522 0.734 45
## 523 0.488 46
## 524 0.358 27
## 525 0.408 36
## 526 0.261 28
## 527 0.223 25
## 528 0.222 26
## 529 1.057 37
## 530 0.258 52
## 531 0.197 26
## 532 0.278 66
## 533 0.766 22
## 534 0.142 33
## 535 0.171 63
## 536 0.340 27
## 537 0.245 30
## 538 0.315 23
##
## $usekernel
## [1] FALSE
##
## $varnames
## [1] "Pregnancies" "Glucose"
## [3] "BloodPressure" "SkinThickness"
## [5] "Insulin" "BMI"
## [7] "DiabetesPedigreeFunction" "Age"
##
## attr(,"class")
## [1] "NaiveBayes"#Plot model
plot(model)
Predict testing set’s data into model
Evaluate outcome using accuracy
#Feature Scaling
train_scale <- scale(data_train[ ,1:8])
test_scale <- scale(data_test[ ,1:8])
#Predict on test data
y_pred <- predict(model, newdata=data_test)## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 79## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 80## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 81## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 82## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 83## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 84## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 86## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 88## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 91## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 92## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 93## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 95## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 96## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 97## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 98## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 99## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 100## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 101## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 102## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 103## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 104## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 105## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 106## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 107## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 108## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 109## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 110## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 111## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 113## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 114## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 115## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 116## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 117## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 118## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 119## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 120## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 121## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 122## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 123## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 124## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 125## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 126## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 127## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 128## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 129## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 130## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 131## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 132## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 133## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 134## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 135## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 136## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 137## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 138## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 139## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 141## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 142## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 150## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 151## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 152## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 153## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 156## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 157## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 158## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 159## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 165## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 166## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 167## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 168## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 169## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 170## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 171## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 173## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 174## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 175## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 177## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 178## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 179## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 180## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 181## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 182## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 183## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 184## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 185## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 186## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 187## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 188## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 189## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 190## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 192## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 193## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 195## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 196## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 197## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 198## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 199## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 200## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 201## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 202## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 203## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 204## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 205## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 206## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 207## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 208## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 209## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 210## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 211## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 212## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 213## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 214## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 215## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 216## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 217## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 218## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 219## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 220## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 221## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 222## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 223## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 224## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 225## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 226## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 227## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 230summary(data_test$Outcome)## 0 1
## 150 80summary(y_pred$class)## 0 1
## 170 60#Build Confusion Matrix
cm <- table(data_test$Outcome, y_pred$class)
cm##
## 0 1
## 0 131 19
## 1 39 41#Model evaluation
confusionMatrix(cm)## Confusion Matrix and Statistics
##
##
## 0 1
## 0 131 19
## 1 39 41
##
## Accuracy : 0.7478
## 95% CI : (0.6865, 0.8026)
## No Information Rate : 0.7391
## P-Value [Acc > NIR] : 0.4154
##
## Kappa : 0.4097
##
## Mcnemar's Test P-Value : 0.0126
##
## Sensitivity : 0.7706
## Specificity : 0.6833
## Pos Pred Value : 0.8733
## Neg Pred Value : 0.5125
## Prevalence : 0.7391
## Detection Rate : 0.5696
## Detection Prevalence : 0.6522
## Balanced Accuracy : 0.7270
##
## 'Positive' Class : 0
## #Accuracy achieved using Naive Bayes model is 74.78%, above the baseline accuracy of 65%.