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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_NaiveBayes.wasp

Title produced by software

Naive Bayes

Date of computation

Thu, 28 Jan 2021 14:30:45 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2021/Jan/28/t1611840940ld3r9t6sgjoh048.htm/, Retrieved Sun, 28 Apr 2024 11:44:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319361, Retrieved Sun, 28 Apr 2024 11:44:08 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

105

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Naive Bayes] [Naive Bayes targe...] [2021-01-28 13:30:45] [bf794a8cc14a008674dc95a1a23d0f77] [Current]

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Dataseries X:

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Histogram

Boxplots

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1 130 "'Male'"
1 130 "'Female'"
1 120 "'Male'"
1 120 "'Female'"
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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319361&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

10 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319361&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319361&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	Big Analytics Cloud Computing Center

Naive Bayes Classifier
> naive_bayes_via_caret$results usekernel laplace adjust Accuracy Kappa AccuracySD KappaSD 1 FALSE 0.0 0.75 0.5930546 0.2102384 0.04243491 0.0885656 2 FALSE 0.0 1.00 0.5930546 0.2102384 0.04243491 0.0885656 3 FALSE 0.0 1.25 0.5930546 0.2102384 0.04243491 0.0885656 4 FALSE 0.0 1.50 0.5930546 0.2102384 0.04243491 0.0885656 5 FALSE 0.0 1.75 0.5930546 0.2102384 0.04243491 0.0885656 6 FALSE 0.0 2.00 0.5930546 0.2102384 0.04243491 0.0885656 7 FALSE 0.0 2.25 0.5930546 0.2102384 0.04243491 0.0885656 8 FALSE 0.0 2.50 0.5930546 0.2102384 0.04243491 0.0885656 49 TRUE 0.0 0.75 NaN NaN NA NA 50 TRUE 0.0 1.00 NaN NaN NA NA 51 TRUE 0.0 1.25 NaN NaN NA NA 52 TRUE 0.0 1.50 NaN NaN NA NA 53 TRUE 0.0 1.75 NaN NaN NA NA 54 TRUE 0.0 2.00 NaN NaN NA NA 55 TRUE 0.0 2.25 NaN NaN NA NA 56 TRUE 0.0 2.50 NaN NaN NA NA 9 FALSE 0.5 0.75 0.5930546 0.2102384 0.04243491 0.0885656 10 FALSE 0.5 1.00 0.5930546 0.2102384 0.04243491 0.0885656 11 FALSE 0.5 1.25 0.5930546 0.2102384 0.04243491 0.0885656 12 FALSE 0.5 1.50 0.5930546 0.2102384 0.04243491 0.0885656 13 FALSE 0.5 1.75 0.5930546 0.2102384 0.04243491 0.0885656 14 FALSE 0.5 2.00 0.5930546 0.2102384 0.04243491 0.0885656 15 FALSE 0.5 2.25 0.5930546 0.2102384 0.04243491 0.0885656 16 FALSE 0.5 2.50 0.5930546 0.2102384 0.04243491 0.0885656 57 TRUE 0.5 0.75 NaN NaN NA NA 58 TRUE 0.5 1.00 NaN NaN NA NA 59 TRUE 0.5 1.25 NaN NaN NA NA 60 TRUE 0.5 1.50 NaN NaN NA NA 61 TRUE 0.5 1.75 NaN NaN NA NA 62 TRUE 0.5 2.00 NaN NaN NA NA 63 TRUE 0.5 2.25 NaN NaN NA NA 64 TRUE 0.5 2.50 NaN NaN NA NA 17 FALSE 1.0 0.75 0.5930546 0.2102384 0.04243491 0.0885656 18 FALSE 1.0 1.00 0.5930546 0.2102384 0.04243491 0.0885656 19 FALSE 1.0 1.25 0.5930546 0.2102384 0.04243491 0.0885656 20 FALSE 1.0 1.50 0.5930546 0.2102384 0.04243491 0.0885656 21 FALSE 1.0 1.75 0.5930546 0.2102384 0.04243491 0.0885656 22 FALSE 1.0 2.00 0.5930546 0.2102384 0.04243491 0.0885656 23 FALSE 1.0 2.25 0.5930546 0.2102384 0.04243491 0.0885656 24 FALSE 1.0 2.50 0.5930546 0.2102384 0.04243491 0.0885656 65 TRUE 1.0 0.75 NaN NaN NA NA 66 TRUE 1.0 1.00 NaN NaN NA NA 67 TRUE 1.0 1.25 NaN NaN NA NA 68 TRUE 1.0 1.50 NaN NaN NA NA 69 TRUE 1.0 1.75 NaN NaN NA NA 70 TRUE 1.0 2.00 NaN NaN NA NA 71 TRUE 1.0 2.25 NaN NaN NA NA 72 TRUE 1.0 2.50 NaN NaN NA NA 25 FALSE 2.0 0.75 0.5930546 0.2102384 0.04243491 0.0885656 26 FALSE 2.0 1.00 0.5930546 0.2102384 0.04243491 0.0885656 27 FALSE 2.0 1.25 0.5930546 0.2102384 0.04243491 0.0885656 28 FALSE 2.0 1.50 0.5930546 0.2102384 0.04243491 0.0885656 29 FALSE 2.0 1.75 0.5930546 0.2102384 0.04243491 0.0885656 30 FALSE 2.0 2.00 0.5930546 0.2102384 0.04243491 0.0885656 31 FALSE 2.0 2.25 0.5930546 0.2102384 0.04243491 0.0885656 32 FALSE 2.0 2.50 0.5930546 0.2102384 0.04243491 0.0885656 73 TRUE 2.0 0.75 NaN NaN NA NA 74 TRUE 2.0 1.00 NaN NaN NA NA 75 TRUE 2.0 1.25 NaN NaN NA NA 76 TRUE 2.0 1.50 NaN NaN NA NA 77 TRUE 2.0 1.75 NaN NaN NA NA 78 TRUE 2.0 2.00 NaN NaN NA NA 79 TRUE 2.0 2.25 NaN NaN NA NA 80 TRUE 2.0 2.50 NaN NaN NA NA 33 FALSE 3.0 0.75 0.5930546 0.2102384 0.04243491 0.0885656 34 FALSE 3.0 1.00 0.5930546 0.2102384 0.04243491 0.0885656 35 FALSE 3.0 1.25 0.5930546 0.2102384 0.04243491 0.0885656 36 FALSE 3.0 1.50 0.5930546 0.2102384 0.04243491 0.0885656 37 FALSE 3.0 1.75 0.5930546 0.2102384 0.04243491 0.0885656 38 FALSE 3.0 2.00 0.5930546 0.2102384 0.04243491 0.0885656 39 FALSE 3.0 2.25 0.5930546 0.2102384 0.04243491 0.0885656 40 FALSE 3.0 2.50 0.5930546 0.2102384 0.04243491 0.0885656 81 TRUE 3.0 0.75 NaN NaN NA NA 82 TRUE 3.0 1.00 NaN NaN NA NA 83 TRUE 3.0 1.25 NaN NaN NA NA 84 TRUE 3.0 1.50 NaN NaN NA NA 85 TRUE 3.0 1.75 NaN NaN NA NA 86 TRUE 3.0 2.00 NaN NaN NA NA 87 TRUE 3.0 2.25 NaN NaN NA NA 88 TRUE 3.0 2.50 NaN NaN NA NA 41 FALSE 4.0 0.75 0.5930546 0.2102384 0.04243491 0.0885656 42 FALSE 4.0 1.00 0.5930546 0.2102384 0.04243491 0.0885656 43 FALSE 4.0 1.25 0.5930546 0.2102384 0.04243491 0.0885656 44 FALSE 4.0 1.50 0.5930546 0.2102384 0.04243491 0.0885656 45 FALSE 4.0 1.75 0.5930546 0.2102384 0.04243491 0.0885656 46 FALSE 4.0 2.00 0.5930546 0.2102384 0.04243491 0.0885656 47 FALSE 4.0 2.25 0.5930546 0.2102384 0.04243491 0.0885656 48 FALSE 4.0 2.50 0.5930546 0.2102384 0.04243491 0.0885656 89 TRUE 4.0 0.75 NaN NaN NA NA 90 TRUE 4.0 1.00 NaN NaN NA NA 91 TRUE 4.0 1.25 NaN NaN NA NA 92 TRUE 4.0 1.50 NaN NaN NA NA 93 TRUE 4.0 1.75 NaN NaN NA NA 94 TRUE 4.0 2.00 NaN NaN NA NA 95 TRUE 4.0 2.25 NaN NaN NA NA 96 TRUE 4.0 2.50 NaN NaN NA NA
> naive_bayes_via_caret$finalModel$tuneValue laplace usekernel adjust 1 0 FALSE 0.75
> print.naive_bayes(naive_bayes_via_caret$finalModel) ================================== Naive Bayes ================================== Call: naive_bayes.default(x = x, y = y, laplace = param$laplace, usekernel = FALSE, usepoisson = TRUE, kernel = ..1, bw = "SJ") --------------------------------------------------------------------------------- Laplace smoothing: 0 --------------------------------------------------------------------------------- A priori probabilities: 0 1 0.4554455 0.5445545 --------------------------------------------------------------------------------- Tables: --------------------------------------------------------------------------------- ::: bloodpressureNum (Gaussian) --------------------------------------------------------------------------------- bloodpressureNum 0 1 mean 134.39855 129.30303 sd 18.72994 16.16961 --------------------------------------------------------------------------------- ::: sexLabel'Male' (Gaussian) --------------------------------------------------------------------------------- sexLabel'Male' 0 1 mean 0.8260870 0.5636364 sd 0.3804155 0.4974436 ---------------------------------------------------------------------------------
> z target bloodpressureNum sexLabel 0 1 1 1 145 'Male' 0.4963321 0.50366788 2 1 130 'Male' 0.4128181 0.58718191 3 1 130 'Female' 0.4128181 0.58718191 4 1 120 'Male' 0.3880164 0.61198357 5 1 120 'Female' 0.3880164 0.61198357 6 1 140 'Male' 0.4621778 0.53782224 7 1 140 'Female' 0.4621778 0.53782224 8 1 120 'Male' 0.3880164 0.61198357 9 1 172 'Male' 0.7586834 0.24131665 10 1 150 'Male' 0.5365819 0.46341807 11 1 140 'Male' 0.4621778 0.53782224 12 1 130 'Female' 0.4128181 0.58718191 13 1 130 'Male' 0.4128181 0.58718191 14 1 110 'Male' 0.3866137 0.61338629 15 1 150 'Female' 0.5365819 0.46341807 16 1 120 'Female' 0.3880164 0.61198357 17 1 120 'Female' 0.3880164 0.61198357 18 1 150 'Female' 0.5365819 0.46341807 19 1 150 'Male' 0.5365819 0.46341807 20 1 140 'Female' 0.4621778 0.53782224 21 1 135 'Male' 0.4343486 0.56565138 22 1 130 'Male' 0.4128181 0.58718191 23 1 140 'Male' 0.4621778 0.53782224 24 1 150 'Male' 0.5365819 0.46341807 25 1 140 'Male' 0.4621778 0.53782224 26 1 160 'Female' 0.6323187 0.36768134 27 1 150 'Male' 0.5365819 0.46341807 28 1 110 'Male' 0.3866137 0.61338629 29 1 140 'Female' 0.4621778 0.53782224 30 1 130 'Male' 0.4128181 0.58718191 31 1 105 'Female' 0.3946059 0.60539414 32 1 120 'Male' 0.3880164 0.61198357 33 1 130 'Male' 0.4128181 0.58718191 34 1 125 'Male' 0.3974336 0.60256642 35 1 125 'Male' 0.3974336 0.60256642 36 1 142 'Female' 0.4750861 0.52491395 37 1 135 'Female' 0.4343486 0.56565138 38 1 150 'Male' 0.5365819 0.46341807 39 1 155 'Female' 0.5822960 0.41770400 40 1 160 'Female' 0.6323187 0.36768134 41 1 140 'Female' 0.4621778 0.53782224 42 1 130 'Male' 0.4128181 0.58718191 43 1 104 'Male' 0.3969106 0.60308940 44 1 130 'Female' 0.4128181 0.58718191 45 1 140 'Male' 0.4621778 0.53782224 46 1 120 'Male' 0.3880164 0.61198357 47 1 140 'Male' 0.4621778 0.53782224 48 1 138 'Male' 0.4502844 0.54971565 49 1 128 'Female' 0.4059371 0.59406295 50 1 138 'Female' 0.4502844 0.54971565 51 1 130 'Female' 0.4128181 0.58718191 52 1 120 'Male' 0.3880164 0.61198357 53 1 130 'Male' 0.4128181 0.58718191 54 1 108 'Female' 0.3891086 0.61089142 55 1 135 'Female' 0.4343486 0.56565138 56 1 134 'Male' 0.4295424 0.57045757 57 1 122 'Male' 0.3910775 0.60892250 58 1 115 'Male' 0.3844291 0.61557088 59 1 118 'Male' 0.3858872 0.61411279 60 1 128 'Female' 0.4059371 0.59406295 61 1 110 'Female' 0.3866137 0.61338629 62 1 108 'Male' 0.3891086 0.61089142 63 1 118 'Male' 0.3858872 0.61411279 64 1 135 'Male' 0.4343486 0.56565138 65 1 140 'Male' 0.4621778 0.53782224 66 1 138 'Female' 0.4502844 0.54971565 67 1 100 'Male' 0.4085263 0.59147370 68 1 130 'Female' 0.4128181 0.58718191 69 1 120 'Male' 0.3880164 0.61198357 70 1 124 'Female' 0.3950781 0.60492192 71 1 120 'Male' 0.3880164 0.61198357 72 1 94 'Male' 0.4333012 0.56669885 73 1 130 'Male' 0.4128181 0.58718191 74 1 140 'Male' 0.4621778 0.53782224 75 1 122 'Female' 0.3910775 0.60892250 76 1 135 'Female' 0.4343486 0.56565138 77 1 125 'Male' 0.3974336 0.60256642 78 1 140 'Male' 0.4621778 0.53782224 79 1 128 'Male' 0.4059371 0.59406295 80 1 105 'Male' 0.3946059 0.60539414 81 1 112 'Male' 0.3850471 0.61495293 82 1 128 'Male' 0.4059371 0.59406295 83 1 102 'Female' 0.4022362 0.59776376 84 1 152 'Male' 0.5542638 0.44573617 85 1 102 'Female' 0.4022362 0.59776376 86 1 115 'Female' 0.3844291 0.61557088 87 1 118 'Male' 0.3858872 0.61411279 88 1 101 'Male' 0.4052600 0.59474002 89 1 110 'Female' 0.3866137 0.61338629 90 1 100 'Female' 0.4085263 0.59147370 91 1 124 'Male' 0.3950781 0.60492192 92 1 132 'Male' 0.4206827 0.57931727 93 1 138 'Male' 0.4502844 0.54971565 94 1 132 'Female' 0.4206827 0.57931727 95 1 112 'Female' 0.3850471 0.61495293 96 1 142 'Male' 0.4750861 0.52491395 97 1 140 'Female' 0.4621778 0.53782224 98 1 108 'Male' 0.3891086 0.61089142 99 1 130 'Male' 0.4128181 0.58718191 100 1 130 'Male' 0.4128181 0.58718191 101 1 148 'Male' 0.5197803 0.48021970 102 1 178 'Male' 0.8175342 0.18246584 103 1 140 'Female' 0.4621778 0.53782224 104 1 120 'Male' 0.3880164 0.61198357 105 1 129 'Male' 0.4092554 0.59074463 106 1 120 'Female' 0.3880164 0.61198357 107 1 160 'Male' 0.6323187 0.36768134 108 1 138 'Female' 0.4502844 0.54971565 109 1 120 'Female' 0.3880164 0.61198357 110 1 110 'Female' 0.3866137 0.61338629 111 1 180 'Female' 0.8355671 0.16443288 112 1 150 'Male' 0.5365819 0.46341807 113 1 140 'Female' 0.4621778 0.53782224 114 1 110 'Male' 0.3866137 0.61338629 115 1 130 'Male' 0.4128181 0.58718191 116 1 120 'Female' 0.3880164 0.61198357 117 1 130 'Male' 0.4128181 0.58718191 118 1 120 'Male' 0.3880164 0.61198357 119 1 105 'Female' 0.3946059 0.60539414 120 1 138 'Female' 0.4502844 0.54971565 121 1 130 'Female' 0.4128181 0.58718191 122 1 138 'Male' 0.4502844 0.54971565 123 1 112 'Female' 0.3850471 0.61495293 124 1 108 'Female' 0.3891086 0.61089142 125 1 94 'Female' 0.4333012 0.56669885 126 1 118 'Female' 0.3858872 0.61411279 127 1 112 'Male' 0.3850471 0.61495293 128 1 152 'Female' 0.5542638 0.44573617 129 1 136 'Female' 0.4394072 0.56059285 130 1 120 'Female' 0.3880164 0.61198357 131 1 160 'Female' 0.6323187 0.36768134 132 1 134 'Female' 0.4295424 0.57045757 133 1 120 'Male' 0.3880164 0.61198357 134 1 110 'Male' 0.3866137 0.61338629 135 1 126 'Female' 0.4000276 0.59997237 136 1 130 'Female' 0.4128181 0.58718191 137 1 120 'Female' 0.3880164 0.61198357 138 1 128 'Male' 0.4059371 0.59406295 139 1 110 'Male' 0.3866137 0.61338629 140 1 128 'Male' 0.4059371 0.59406295 141 1 120 'Female' 0.3880164 0.61198357 142 1 115 'Male' 0.3844291 0.61557088 143 1 120 'Female' 0.3880164 0.61198357 144 1 106 'Female' 0.3925381 0.60746193 145 1 140 'Female' 0.4621778 0.53782224 146 1 156 'Male' 0.5920030 0.40799705 147 1 118 'Female' 0.3858872 0.61411279 148 1 150 'Female' 0.5365819 0.46341807 149 1 120 'Male' 0.3880164 0.61198357 150 1 130 'Male' 0.4128181 0.58718191 151 1 160 'Male' 0.6323187 0.36768134 152 1 112 'Female' 0.3850471 0.61495293 153 1 170 'Male' 0.7379195 0.26208050 154 1 146 'Female' 0.5039079 0.49609205 155 1 138 'Female' 0.4502844 0.54971565 156 1 130 'Female' 0.4128181 0.58718191 157 1 130 'Male' 0.4128181 0.58718191 158 1 122 'Male' 0.3910775 0.60892250 159 1 125 'Male' 0.3974336 0.60256642 160 1 130 'Male' 0.4128181 0.58718191 161 1 120 'Male' 0.3880164 0.61198357 162 1 132 'Female' 0.4206827 0.57931727 163 1 120 'Male' 0.3880164 0.61198357 164 1 138 'Male' 0.4502844 0.54971565 165 1 138 'Male' 0.4502844 0.54971565 166 0 160 'Male' 0.6323187 0.36768134 167 0 120 'Male' 0.3880164 0.61198357 168 0 140 'Female' 0.4621778 0.53782224 169 0 130 'Male' 0.4128181 0.58718191 170 0 140 'Male' 0.4621778 0.53782224 171 0 130 'Male' 0.4128181 0.58718191 172 0 110 'Male' 0.3866137 0.61338629 173 0 120 'Male' 0.3880164 0.61198357 174 0 132 'Male' 0.4206827 0.57931727 175 0 130 'Male' 0.4128181 0.58718191 176 0 110 'Male' 0.3866137 0.61338629 177 0 117 'Male' 0.3851702 0.61482978 178 0 140 'Male' 0.4621778 0.53782224 179 0 120 'Male' 0.3880164 0.61198357 180 0 150 'Male' 0.5365819 0.46341807 181 0 132 'Male' 0.4206827 0.57931727 182 0 150 'Female' 0.5365819 0.46341807 183 0 130 'Female' 0.4128181 0.58718191 184 0 112 'Male' 0.3850471 0.61495293 185 0 150 'Male' 0.5365819 0.46341807 186 0 112 'Male' 0.3850471 0.61495293 187 0 130 'Male' 0.4128181 0.58718191 188 0 124 'Male' 0.3950781 0.60492192 189 0 140 'Male' 0.4621778 0.53782224 190 0 110 'Male' 0.3866137 0.61338629 191 0 130 'Female' 0.4128181 0.58718191 192 0 128 'Male' 0.4059371 0.59406295 193 0 120 'Male' 0.3880164 0.61198357 194 0 145 'Male' 0.4963321 0.50366788 195 0 140 'Male' 0.4621778 0.53782224 196 0 170 'Male' 0.7379195 0.26208050 197 0 150 'Male' 0.5365819 0.46341807 198 0 125 'Male' 0.3974336 0.60256642 199 0 120 'Male' 0.3880164 0.61198357 200 0 110 'Male' 0.3866137 0.61338629 201 0 110 'Male' 0.3866137 0.61338629 202 0 125 'Male' 0.3974336 0.60256642 203 0 150 'Male' 0.5365819 0.46341807 204 0 180 'Male' 0.8355671 0.16443288 205 0 160 'Female' 0.6323187 0.36768134 206 0 128 'Male' 0.4059371 0.59406295 207 0 110 'Male' 0.3866137 0.61338629 208 0 150 'Female' 0.5365819 0.46341807 209 0 120 'Male' 0.3880164 0.61198357 210 0 140 'Male' 0.4621778 0.53782224 211 0 128 'Male' 0.4059371 0.59406295 212 0 120 'Male' 0.3880164 0.61198357 213 0 118 'Male' 0.3858872 0.61411279 214 0 145 'Female' 0.4963321 0.50366788 215 0 125 'Male' 0.3974336 0.60256642 216 0 132 'Female' 0.4206827 0.57931727 217 0 130 'Female' 0.4128181 0.58718191 218 0 130 'Male' 0.4128181 0.58718191 219 0 135 'Male' 0.4343486 0.56565138 220 0 130 'Male' 0.4128181 0.58718191 221 0 150 'Female' 0.5365819 0.46341807 222 0 140 'Male' 0.4621778 0.53782224 223 0 138 'Male' 0.4502844 0.54971565 224 0 200 'Female' 0.9568397 0.04316026 225 0 110 'Male' 0.3866137 0.61338629 226 0 145 'Male' 0.4963321 0.50366788 227 0 120 'Male' 0.3880164 0.61198357 228 0 120 'Male' 0.3880164 0.61198357 229 0 170 'Male' 0.7379195 0.26208050 230 0 125 'Male' 0.3974336 0.60256642 231 0 108 'Male' 0.3891086 0.61089142 232 0 165 'Male' 0.6849258 0.31507416 233 0 160 'Male' 0.6323187 0.36768134 234 0 120 'Male' 0.3880164 0.61198357 235 0 130 'Male' 0.4128181 0.58718191 236 0 140 'Male' 0.4621778 0.53782224 237 0 125 'Male' 0.3974336 0.60256642 238 0 140 'Male' 0.4621778 0.53782224 239 0 125 'Male' 0.3974336 0.60256642 240 0 126 'Male' 0.4000276 0.59997237 241 0 160 'Male' 0.6323187 0.36768134 242 0 174 'Female' 0.7789681 0.22103194 243 0 145 'Male' 0.4963321 0.50366788 244 0 152 'Male' 0.5542638 0.44573617 245 0 132 'Male' 0.4206827 0.57931727 246 0 124 'Male' 0.3950781 0.60492192 247 0 134 'Female' 0.4295424 0.57045757 248 0 160 'Male' 0.6323187 0.36768134 249 0 192 'Male' 0.9214861 0.07851393 250 0 140 'Male' 0.4621778 0.53782224 251 0 140 'Male' 0.4621778 0.53782224 252 0 132 'Male' 0.4206827 0.57931727 253 0 138 'Female' 0.4502844 0.54971565 254 0 100 'Male' 0.4085263 0.59147370 255 0 160 'Male' 0.6323187 0.36768134 256 0 142 'Male' 0.4750861 0.52491395 257 0 128 'Male' 0.4059371 0.59406295 258 0 144 'Male' 0.4890014 0.51099861 259 0 150 'Female' 0.5365819 0.46341807 260 0 120 'Male' 0.3880164 0.61198357 261 0 178 'Female' 0.8175342 0.18246584 262 0 112 'Male' 0.3850471 0.61495293 263 0 123 'Male' 0.3929598 0.60704020 264 0 108 'Female' 0.3891086 0.61089142 265 0 110 'Male' 0.3866137 0.61338629 266 0 112 'Male' 0.3850471 0.61495293 267 0 180 'Female' 0.8355671 0.16443288 268 0 118 'Male' 0.3858872 0.61411279 269 0 122 'Male' 0.3910775 0.60892250 270 0 130 'Male' 0.4128181 0.58718191 271 0 120 'Male' 0.3880164 0.61198357 272 0 134 'Male' 0.4295424 0.57045757 273 0 120 'Male' 0.3880164 0.61198357 274 0 100 'Male' 0.4085263 0.59147370 275 0 110 'Male' 0.3866137 0.61338629 276 0 125 'Male' 0.3974336 0.60256642 277 0 146 'Male' 0.5039079 0.49609205 278 0 124 'Male' 0.3950781 0.60492192 279 0 136 'Female' 0.4394072 0.56059285 280 0 138 'Male' 0.4502844 0.54971565 281 0 136 'Male' 0.4394072 0.56059285 282 0 128 'Male' 0.4059371 0.59406295 283 0 126 'Male' 0.4000276 0.59997237 284 0 152 'Male' 0.5542638 0.44573617 285 0 140 'Male' 0.4621778 0.53782224 286 0 140 'Male' 0.4621778 0.53782224 287 0 134 'Male' 0.4295424 0.57045757 288 0 154 'Male' 0.5727633 0.42723673 289 0 110 'Male' 0.3866137 0.61338629 290 0 128 'Female' 0.4059371 0.59406295 291 0 148 'Male' 0.5197803 0.48021970 292 0 114 'Male' 0.3844045 0.61559550 293 0 170 'Female' 0.7379195 0.26208050 294 0 152 'Male' 0.5542638 0.44573617 295 0 120 'Male' 0.3880164 0.61198357 296 0 140 'Male' 0.4621778 0.53782224 297 0 124 'Female' 0.3950781 0.60492192 298 0 164 'Male' 0.6742979 0.32570206 299 0 140 'Female' 0.4621778 0.53782224 300 0 110 'Male' 0.3866137 0.61338629 301 0 144 'Male' 0.4890014 0.51099861 302 0 130 'Male' 0.4128181 0.58718191 303 0 130 'Female' 0.4128181 0.58718191

\begin{tabular}{lllllllll}
\hline
Naive Bayes Classifier \tabularnewline
> naive_bayes_via_caret$results
   usekernel laplace adjust  Accuracy     Kappa AccuracySD   KappaSD
1      FALSE     0.0   0.75 0.5930546 0.2102384 0.04243491 0.0885656
2      FALSE     0.0   1.00 0.5930546 0.2102384 0.04243491 0.0885656
3      FALSE     0.0   1.25 0.5930546 0.2102384 0.04243491 0.0885656
4      FALSE     0.0   1.50 0.5930546 0.2102384 0.04243491 0.0885656
5      FALSE     0.0   1.75 0.5930546 0.2102384 0.04243491 0.0885656
6      FALSE     0.0   2.00 0.5930546 0.2102384 0.04243491 0.0885656
7      FALSE     0.0   2.25 0.5930546 0.2102384 0.04243491 0.0885656
8      FALSE     0.0   2.50 0.5930546 0.2102384 0.04243491 0.0885656
49      TRUE     0.0   0.75       NaN       NaN         NA        NA
50      TRUE     0.0   1.00       NaN       NaN         NA        NA
51      TRUE     0.0   1.25       NaN       NaN         NA        NA
52      TRUE     0.0   1.50       NaN       NaN         NA        NA
53      TRUE     0.0   1.75       NaN       NaN         NA        NA
54      TRUE     0.0   2.00       NaN       NaN         NA        NA
55      TRUE     0.0   2.25       NaN       NaN         NA        NA
56      TRUE     0.0   2.50       NaN       NaN         NA        NA
9      FALSE     0.5   0.75 0.5930546 0.2102384 0.04243491 0.0885656
10     FALSE     0.5   1.00 0.5930546 0.2102384 0.04243491 0.0885656
11     FALSE     0.5   1.25 0.5930546 0.2102384 0.04243491 0.0885656
12     FALSE     0.5   1.50 0.5930546 0.2102384 0.04243491 0.0885656
13     FALSE     0.5   1.75 0.5930546 0.2102384 0.04243491 0.0885656
14     FALSE     0.5   2.00 0.5930546 0.2102384 0.04243491 0.0885656
15     FALSE     0.5   2.25 0.5930546 0.2102384 0.04243491 0.0885656
16     FALSE     0.5   2.50 0.5930546 0.2102384 0.04243491 0.0885656
57      TRUE     0.5   0.75       NaN       NaN         NA        NA
58      TRUE     0.5   1.00       NaN       NaN         NA        NA
59      TRUE     0.5   1.25       NaN       NaN         NA        NA
60      TRUE     0.5   1.50       NaN       NaN         NA        NA
61      TRUE     0.5   1.75       NaN       NaN         NA        NA
62      TRUE     0.5   2.00       NaN       NaN         NA        NA
63      TRUE     0.5   2.25       NaN       NaN         NA        NA
64      TRUE     0.5   2.50       NaN       NaN         NA        NA
17     FALSE     1.0   0.75 0.5930546 0.2102384 0.04243491 0.0885656
18     FALSE     1.0   1.00 0.5930546 0.2102384 0.04243491 0.0885656
19     FALSE     1.0   1.25 0.5930546 0.2102384 0.04243491 0.0885656
20     FALSE     1.0   1.50 0.5930546 0.2102384 0.04243491 0.0885656
21     FALSE     1.0   1.75 0.5930546 0.2102384 0.04243491 0.0885656
22     FALSE     1.0   2.00 0.5930546 0.2102384 0.04243491 0.0885656
23     FALSE     1.0   2.25 0.5930546 0.2102384 0.04243491 0.0885656
24     FALSE     1.0   2.50 0.5930546 0.2102384 0.04243491 0.0885656
65      TRUE     1.0   0.75       NaN       NaN         NA        NA
66      TRUE     1.0   1.00       NaN       NaN         NA        NA
67      TRUE     1.0   1.25       NaN       NaN         NA        NA
68      TRUE     1.0   1.50       NaN       NaN         NA        NA
69      TRUE     1.0   1.75       NaN       NaN         NA        NA
70      TRUE     1.0   2.00       NaN       NaN         NA        NA
71      TRUE     1.0   2.25       NaN       NaN         NA        NA
72      TRUE     1.0   2.50       NaN       NaN         NA        NA
25     FALSE     2.0   0.75 0.5930546 0.2102384 0.04243491 0.0885656
26     FALSE     2.0   1.00 0.5930546 0.2102384 0.04243491 0.0885656
27     FALSE     2.0   1.25 0.5930546 0.2102384 0.04243491 0.0885656
28     FALSE     2.0   1.50 0.5930546 0.2102384 0.04243491 0.0885656
29     FALSE     2.0   1.75 0.5930546 0.2102384 0.04243491 0.0885656
30     FALSE     2.0   2.00 0.5930546 0.2102384 0.04243491 0.0885656
31     FALSE     2.0   2.25 0.5930546 0.2102384 0.04243491 0.0885656
32     FALSE     2.0   2.50 0.5930546 0.2102384 0.04243491 0.0885656
73      TRUE     2.0   0.75       NaN       NaN         NA        NA
74      TRUE     2.0   1.00       NaN       NaN         NA        NA
75      TRUE     2.0   1.25       NaN       NaN         NA        NA
76      TRUE     2.0   1.50       NaN       NaN         NA        NA
77      TRUE     2.0   1.75       NaN       NaN         NA        NA
78      TRUE     2.0   2.00       NaN       NaN         NA        NA
79      TRUE     2.0   2.25       NaN       NaN         NA        NA
80      TRUE     2.0   2.50       NaN       NaN         NA        NA
33     FALSE     3.0   0.75 0.5930546 0.2102384 0.04243491 0.0885656
34     FALSE     3.0   1.00 0.5930546 0.2102384 0.04243491 0.0885656
35     FALSE     3.0   1.25 0.5930546 0.2102384 0.04243491 0.0885656
36     FALSE     3.0   1.50 0.5930546 0.2102384 0.04243491 0.0885656
37     FALSE     3.0   1.75 0.5930546 0.2102384 0.04243491 0.0885656
38     FALSE     3.0   2.00 0.5930546 0.2102384 0.04243491 0.0885656
39     FALSE     3.0   2.25 0.5930546 0.2102384 0.04243491 0.0885656
40     FALSE     3.0   2.50 0.5930546 0.2102384 0.04243491 0.0885656
81      TRUE     3.0   0.75       NaN       NaN         NA        NA
82      TRUE     3.0   1.00       NaN       NaN         NA        NA
83      TRUE     3.0   1.25       NaN       NaN         NA        NA
84      TRUE     3.0   1.50       NaN       NaN         NA        NA
85      TRUE     3.0   1.75       NaN       NaN         NA        NA
86      TRUE     3.0   2.00       NaN       NaN         NA        NA
87      TRUE     3.0   2.25       NaN       NaN         NA        NA
88      TRUE     3.0   2.50       NaN       NaN         NA        NA
41     FALSE     4.0   0.75 0.5930546 0.2102384 0.04243491 0.0885656
42     FALSE     4.0   1.00 0.5930546 0.2102384 0.04243491 0.0885656
43     FALSE     4.0   1.25 0.5930546 0.2102384 0.04243491 0.0885656
44     FALSE     4.0   1.50 0.5930546 0.2102384 0.04243491 0.0885656
45     FALSE     4.0   1.75 0.5930546 0.2102384 0.04243491 0.0885656
46     FALSE     4.0   2.00 0.5930546 0.2102384 0.04243491 0.0885656
47     FALSE     4.0   2.25 0.5930546 0.2102384 0.04243491 0.0885656
48     FALSE     4.0   2.50 0.5930546 0.2102384 0.04243491 0.0885656
89      TRUE     4.0   0.75       NaN       NaN         NA        NA
90      TRUE     4.0   1.00       NaN       NaN         NA        NA
91      TRUE     4.0   1.25       NaN       NaN         NA        NA
92      TRUE     4.0   1.50       NaN       NaN         NA        NA
93      TRUE     4.0   1.75       NaN       NaN         NA        NA
94      TRUE     4.0   2.00       NaN       NaN         NA        NA
95      TRUE     4.0   2.25       NaN       NaN         NA        NA
96      TRUE     4.0   2.50       NaN       NaN         NA        NA
 \tabularnewline
> naive_bayes_via_caret$finalModel$tuneValue
  laplace usekernel adjust
1       0     FALSE   0.75
 \tabularnewline
> print.naive_bayes(naive_bayes_via_caret$finalModel)
================================== Naive Bayes ================================== 
 
 Call: 
naive_bayes.default(x = x, y = y, laplace = param$laplace, usekernel = FALSE, 
    usepoisson = TRUE, kernel = ..1, bw = "SJ")
--------------------------------------------------------------------------------- 
 
Laplace smoothing: 0
--------------------------------------------------------------------------------- 
 
 A priori probabilities: 
        0         1 
0.4554455 0.5445545 
--------------------------------------------------------------------------------- 
 
 Tables: 
--------------------------------------------------------------------------------- 
 ::: bloodpressureNum (Gaussian) 
--------------------------------------------------------------------------------- 
                
bloodpressureNum         0         1
            mean 134.39855 129.30303
            sd    18.72994  16.16961
--------------------------------------------------------------------------------- 
 ::: sexLabel'Male' (Gaussian) 
--------------------------------------------------------------------------------- 
              
sexLabel'Male'         0         1
          mean 0.8260870 0.5636364
          sd   0.3804155 0.4974436
---------------------------------------------------------------------------------
 \tabularnewline
> z
    target bloodpressureNum sexLabel         0          1
1        1              145   'Male' 0.4963321 0.50366788
2        1              130   'Male' 0.4128181 0.58718191
3        1              130 'Female' 0.4128181 0.58718191
4        1              120   'Male' 0.3880164 0.61198357
5        1              120 'Female' 0.3880164 0.61198357
6        1              140   'Male' 0.4621778 0.53782224
7        1              140 'Female' 0.4621778 0.53782224
8        1              120   'Male' 0.3880164 0.61198357
9        1              172   'Male' 0.7586834 0.24131665
10       1              150   'Male' 0.5365819 0.46341807
11       1              140   'Male' 0.4621778 0.53782224
12       1              130 'Female' 0.4128181 0.58718191
13       1              130   'Male' 0.4128181 0.58718191
14       1              110   'Male' 0.3866137 0.61338629
15       1              150 'Female' 0.5365819 0.46341807
16       1              120 'Female' 0.3880164 0.61198357
17       1              120 'Female' 0.3880164 0.61198357
18       1              150 'Female' 0.5365819 0.46341807
19       1              150   'Male' 0.5365819 0.46341807
20       1              140 'Female' 0.4621778 0.53782224
21       1              135   'Male' 0.4343486 0.56565138
22       1              130   'Male' 0.4128181 0.58718191
23       1              140   'Male' 0.4621778 0.53782224
24       1              150   'Male' 0.5365819 0.46341807
25       1              140   'Male' 0.4621778 0.53782224
26       1              160 'Female' 0.6323187 0.36768134
27       1              150   'Male' 0.5365819 0.46341807
28       1              110   'Male' 0.3866137 0.61338629
29       1              140 'Female' 0.4621778 0.53782224
30       1              130   'Male' 0.4128181 0.58718191
31       1              105 'Female' 0.3946059 0.60539414
32       1              120   'Male' 0.3880164 0.61198357
33       1              130   'Male' 0.4128181 0.58718191
34       1              125   'Male' 0.3974336 0.60256642
35       1              125   'Male' 0.3974336 0.60256642
36       1              142 'Female' 0.4750861 0.52491395
37       1              135 'Female' 0.4343486 0.56565138
38       1              150   'Male' 0.5365819 0.46341807
39       1              155 'Female' 0.5822960 0.41770400
40       1              160 'Female' 0.6323187 0.36768134
41       1              140 'Female' 0.4621778 0.53782224
42       1              130   'Male' 0.4128181 0.58718191
43       1              104   'Male' 0.3969106 0.60308940
44       1              130 'Female' 0.4128181 0.58718191
45       1              140   'Male' 0.4621778 0.53782224
46       1              120   'Male' 0.3880164 0.61198357
47       1              140   'Male' 0.4621778 0.53782224
48       1              138   'Male' 0.4502844 0.54971565
49       1              128 'Female' 0.4059371 0.59406295
50       1              138 'Female' 0.4502844 0.54971565
51       1              130 'Female' 0.4128181 0.58718191
52       1              120   'Male' 0.3880164 0.61198357
53       1              130   'Male' 0.4128181 0.58718191
54       1              108 'Female' 0.3891086 0.61089142
55       1              135 'Female' 0.4343486 0.56565138
56       1              134   'Male' 0.4295424 0.57045757
57       1              122   'Male' 0.3910775 0.60892250
58       1              115   'Male' 0.3844291 0.61557088
59       1              118   'Male' 0.3858872 0.61411279
60       1              128 'Female' 0.4059371 0.59406295
61       1              110 'Female' 0.3866137 0.61338629
62       1              108   'Male' 0.3891086 0.61089142
63       1              118   'Male' 0.3858872 0.61411279
64       1              135   'Male' 0.4343486 0.56565138
65       1              140   'Male' 0.4621778 0.53782224
66       1              138 'Female' 0.4502844 0.54971565
67       1              100   'Male' 0.4085263 0.59147370
68       1              130 'Female' 0.4128181 0.58718191
69       1              120   'Male' 0.3880164 0.61198357
70       1              124 'Female' 0.3950781 0.60492192
71       1              120   'Male' 0.3880164 0.61198357
72       1               94   'Male' 0.4333012 0.56669885
73       1              130   'Male' 0.4128181 0.58718191
74       1              140   'Male' 0.4621778 0.53782224
75       1              122 'Female' 0.3910775 0.60892250
76       1              135 'Female' 0.4343486 0.56565138
77       1              125   'Male' 0.3974336 0.60256642
78       1              140   'Male' 0.4621778 0.53782224
79       1              128   'Male' 0.4059371 0.59406295
80       1              105   'Male' 0.3946059 0.60539414
81       1              112   'Male' 0.3850471 0.61495293
82       1              128   'Male' 0.4059371 0.59406295
83       1              102 'Female' 0.4022362 0.59776376
84       1              152   'Male' 0.5542638 0.44573617
85       1              102 'Female' 0.4022362 0.59776376
86       1              115 'Female' 0.3844291 0.61557088
87       1              118   'Male' 0.3858872 0.61411279
88       1              101   'Male' 0.4052600 0.59474002
89       1              110 'Female' 0.3866137 0.61338629
90       1              100 'Female' 0.4085263 0.59147370
91       1              124   'Male' 0.3950781 0.60492192
92       1              132   'Male' 0.4206827 0.57931727
93       1              138   'Male' 0.4502844 0.54971565
94       1              132 'Female' 0.4206827 0.57931727
95       1              112 'Female' 0.3850471 0.61495293
96       1              142   'Male' 0.4750861 0.52491395
97       1              140 'Female' 0.4621778 0.53782224
98       1              108   'Male' 0.3891086 0.61089142
99       1              130   'Male' 0.4128181 0.58718191
100      1              130   'Male' 0.4128181 0.58718191
101      1              148   'Male' 0.5197803 0.48021970
102      1              178   'Male' 0.8175342 0.18246584
103      1              140 'Female' 0.4621778 0.53782224
104      1              120   'Male' 0.3880164 0.61198357
105      1              129   'Male' 0.4092554 0.59074463
106      1              120 'Female' 0.3880164 0.61198357
107      1              160   'Male' 0.6323187 0.36768134
108      1              138 'Female' 0.4502844 0.54971565
109      1              120 'Female' 0.3880164 0.61198357
110      1              110 'Female' 0.3866137 0.61338629
111      1              180 'Female' 0.8355671 0.16443288
112      1              150   'Male' 0.5365819 0.46341807
113      1              140 'Female' 0.4621778 0.53782224
114      1              110   'Male' 0.3866137 0.61338629
115      1              130   'Male' 0.4128181 0.58718191
116      1              120 'Female' 0.3880164 0.61198357
117      1              130   'Male' 0.4128181 0.58718191
118      1              120   'Male' 0.3880164 0.61198357
119      1              105 'Female' 0.3946059 0.60539414
120      1              138 'Female' 0.4502844 0.54971565
121      1              130 'Female' 0.4128181 0.58718191
122      1              138   'Male' 0.4502844 0.54971565
123      1              112 'Female' 0.3850471 0.61495293
124      1              108 'Female' 0.3891086 0.61089142
125      1               94 'Female' 0.4333012 0.56669885
126      1              118 'Female' 0.3858872 0.61411279
127      1              112   'Male' 0.3850471 0.61495293
128      1              152 'Female' 0.5542638 0.44573617
129      1              136 'Female' 0.4394072 0.56059285
130      1              120 'Female' 0.3880164 0.61198357
131      1              160 'Female' 0.6323187 0.36768134
132      1              134 'Female' 0.4295424 0.57045757
133      1              120   'Male' 0.3880164 0.61198357
134      1              110   'Male' 0.3866137 0.61338629
135      1              126 'Female' 0.4000276 0.59997237
136      1              130 'Female' 0.4128181 0.58718191
137      1              120 'Female' 0.3880164 0.61198357
138      1              128   'Male' 0.4059371 0.59406295
139      1              110   'Male' 0.3866137 0.61338629
140      1              128   'Male' 0.4059371 0.59406295
141      1              120 'Female' 0.3880164 0.61198357
142      1              115   'Male' 0.3844291 0.61557088
143      1              120 'Female' 0.3880164 0.61198357
144      1              106 'Female' 0.3925381 0.60746193
145      1              140 'Female' 0.4621778 0.53782224
146      1              156   'Male' 0.5920030 0.40799705
147      1              118 'Female' 0.3858872 0.61411279
148      1              150 'Female' 0.5365819 0.46341807
149      1              120   'Male' 0.3880164 0.61198357
150      1              130   'Male' 0.4128181 0.58718191
151      1              160   'Male' 0.6323187 0.36768134
152      1              112 'Female' 0.3850471 0.61495293
153      1              170   'Male' 0.7379195 0.26208050
154      1              146 'Female' 0.5039079 0.49609205
155      1              138 'Female' 0.4502844 0.54971565
156      1              130 'Female' 0.4128181 0.58718191
157      1              130   'Male' 0.4128181 0.58718191
158      1              122   'Male' 0.3910775 0.60892250
159      1              125   'Male' 0.3974336 0.60256642
160      1              130   'Male' 0.4128181 0.58718191
161      1              120   'Male' 0.3880164 0.61198357
162      1              132 'Female' 0.4206827 0.57931727
163      1              120   'Male' 0.3880164 0.61198357
164      1              138   'Male' 0.4502844 0.54971565
165      1              138   'Male' 0.4502844 0.54971565
166      0              160   'Male' 0.6323187 0.36768134
167      0              120   'Male' 0.3880164 0.61198357
168      0              140 'Female' 0.4621778 0.53782224
169      0              130   'Male' 0.4128181 0.58718191
170      0              140   'Male' 0.4621778 0.53782224
171      0              130   'Male' 0.4128181 0.58718191
172      0              110   'Male' 0.3866137 0.61338629
173      0              120   'Male' 0.3880164 0.61198357
174      0              132   'Male' 0.4206827 0.57931727
175      0              130   'Male' 0.4128181 0.58718191
176      0              110   'Male' 0.3866137 0.61338629
177      0              117   'Male' 0.3851702 0.61482978
178      0              140   'Male' 0.4621778 0.53782224
179      0              120   'Male' 0.3880164 0.61198357
180      0              150   'Male' 0.5365819 0.46341807
181      0              132   'Male' 0.4206827 0.57931727
182      0              150 'Female' 0.5365819 0.46341807
183      0              130 'Female' 0.4128181 0.58718191
184      0              112   'Male' 0.3850471 0.61495293
185      0              150   'Male' 0.5365819 0.46341807
186      0              112   'Male' 0.3850471 0.61495293
187      0              130   'Male' 0.4128181 0.58718191
188      0              124   'Male' 0.3950781 0.60492192
189      0              140   'Male' 0.4621778 0.53782224
190      0              110   'Male' 0.3866137 0.61338629
191      0              130 'Female' 0.4128181 0.58718191
192      0              128   'Male' 0.4059371 0.59406295
193      0              120   'Male' 0.3880164 0.61198357
194      0              145   'Male' 0.4963321 0.50366788
195      0              140   'Male' 0.4621778 0.53782224
196      0              170   'Male' 0.7379195 0.26208050
197      0              150   'Male' 0.5365819 0.46341807
198      0              125   'Male' 0.3974336 0.60256642
199      0              120   'Male' 0.3880164 0.61198357
200      0              110   'Male' 0.3866137 0.61338629
201      0              110   'Male' 0.3866137 0.61338629
202      0              125   'Male' 0.3974336 0.60256642
203      0              150   'Male' 0.5365819 0.46341807
204      0              180   'Male' 0.8355671 0.16443288
205      0              160 'Female' 0.6323187 0.36768134
206      0              128   'Male' 0.4059371 0.59406295
207      0              110   'Male' 0.3866137 0.61338629
208      0              150 'Female' 0.5365819 0.46341807
209      0              120   'Male' 0.3880164 0.61198357
210      0              140   'Male' 0.4621778 0.53782224
211      0              128   'Male' 0.4059371 0.59406295
212      0              120   'Male' 0.3880164 0.61198357
213      0              118   'Male' 0.3858872 0.61411279
214      0              145 'Female' 0.4963321 0.50366788
215      0              125   'Male' 0.3974336 0.60256642
216      0              132 'Female' 0.4206827 0.57931727
217      0              130 'Female' 0.4128181 0.58718191
218      0              130   'Male' 0.4128181 0.58718191
219      0              135   'Male' 0.4343486 0.56565138
220      0              130   'Male' 0.4128181 0.58718191
221      0              150 'Female' 0.5365819 0.46341807
222      0              140   'Male' 0.4621778 0.53782224
223      0              138   'Male' 0.4502844 0.54971565
224      0              200 'Female' 0.9568397 0.04316026
225      0              110   'Male' 0.3866137 0.61338629
226      0              145   'Male' 0.4963321 0.50366788
227      0              120   'Male' 0.3880164 0.61198357
228      0              120   'Male' 0.3880164 0.61198357
229      0              170   'Male' 0.7379195 0.26208050
230      0              125   'Male' 0.3974336 0.60256642
231      0              108   'Male' 0.3891086 0.61089142
232      0              165   'Male' 0.6849258 0.31507416
233      0              160   'Male' 0.6323187 0.36768134
234      0              120   'Male' 0.3880164 0.61198357
235      0              130   'Male' 0.4128181 0.58718191
236      0              140   'Male' 0.4621778 0.53782224
237      0              125   'Male' 0.3974336 0.60256642
238      0              140   'Male' 0.4621778 0.53782224
239      0              125   'Male' 0.3974336 0.60256642
240      0              126   'Male' 0.4000276 0.59997237
241      0              160   'Male' 0.6323187 0.36768134
242      0              174 'Female' 0.7789681 0.22103194
243      0              145   'Male' 0.4963321 0.50366788
244      0              152   'Male' 0.5542638 0.44573617
245      0              132   'Male' 0.4206827 0.57931727
246      0              124   'Male' 0.3950781 0.60492192
247      0              134 'Female' 0.4295424 0.57045757
248      0              160   'Male' 0.6323187 0.36768134
249      0              192   'Male' 0.9214861 0.07851393
250      0              140   'Male' 0.4621778 0.53782224
251      0              140   'Male' 0.4621778 0.53782224
252      0              132   'Male' 0.4206827 0.57931727
253      0              138 'Female' 0.4502844 0.54971565
254      0              100   'Male' 0.4085263 0.59147370
255      0              160   'Male' 0.6323187 0.36768134
256      0              142   'Male' 0.4750861 0.52491395
257      0              128   'Male' 0.4059371 0.59406295
258      0              144   'Male' 0.4890014 0.51099861
259      0              150 'Female' 0.5365819 0.46341807
260      0              120   'Male' 0.3880164 0.61198357
261      0              178 'Female' 0.8175342 0.18246584
262      0              112   'Male' 0.3850471 0.61495293
263      0              123   'Male' 0.3929598 0.60704020
264      0              108 'Female' 0.3891086 0.61089142
265      0              110   'Male' 0.3866137 0.61338629
266      0              112   'Male' 0.3850471 0.61495293
267      0              180 'Female' 0.8355671 0.16443288
268      0              118   'Male' 0.3858872 0.61411279
269      0              122   'Male' 0.3910775 0.60892250
270      0              130   'Male' 0.4128181 0.58718191
271      0              120   'Male' 0.3880164 0.61198357
272      0              134   'Male' 0.4295424 0.57045757
273      0              120   'Male' 0.3880164 0.61198357
274      0              100   'Male' 0.4085263 0.59147370
275      0              110   'Male' 0.3866137 0.61338629
276      0              125   'Male' 0.3974336 0.60256642
277      0              146   'Male' 0.5039079 0.49609205
278      0              124   'Male' 0.3950781 0.60492192
279      0              136 'Female' 0.4394072 0.56059285
280      0              138   'Male' 0.4502844 0.54971565
281      0              136   'Male' 0.4394072 0.56059285
282      0              128   'Male' 0.4059371 0.59406295
283      0              126   'Male' 0.4000276 0.59997237
284      0              152   'Male' 0.5542638 0.44573617
285      0              140   'Male' 0.4621778 0.53782224
286      0              140   'Male' 0.4621778 0.53782224
287      0              134   'Male' 0.4295424 0.57045757
288      0              154   'Male' 0.5727633 0.42723673
289      0              110   'Male' 0.3866137 0.61338629
290      0              128 'Female' 0.4059371 0.59406295
291      0              148   'Male' 0.5197803 0.48021970
292      0              114   'Male' 0.3844045 0.61559550
293      0              170 'Female' 0.7379195 0.26208050
294      0              152   'Male' 0.5542638 0.44573617
295      0              120   'Male' 0.3880164 0.61198357
296      0              140   'Male' 0.4621778 0.53782224
297      0              124 'Female' 0.3950781 0.60492192
298      0              164   'Male' 0.6742979 0.32570206
299      0              140 'Female' 0.4621778 0.53782224
300      0              110   'Male' 0.3866137 0.61338629
301      0              144   'Male' 0.4890014 0.51099861
302      0              130   'Male' 0.4128181 0.58718191
303      0              130 'Female' 0.4128181 0.58718191
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319361&T=1

[TABLE]
[ROW][C]Naive Bayes Classifier[/C][/ROW]
[ROW][C]> naive_bayes_via_caret$results
   usekernel laplace adjust  Accuracy     Kappa AccuracySD   KappaSD
1      FALSE     0.0   0.75 0.5930546 0.2102384 0.04243491 0.0885656
2      FALSE     0.0   1.00 0.5930546 0.2102384 0.04243491 0.0885656
3      FALSE     0.0   1.25 0.5930546 0.2102384 0.04243491 0.0885656
4      FALSE     0.0   1.50 0.5930546 0.2102384 0.04243491 0.0885656
5      FALSE     0.0   1.75 0.5930546 0.2102384 0.04243491 0.0885656
6      FALSE     0.0   2.00 0.5930546 0.2102384 0.04243491 0.0885656
7      FALSE     0.0   2.25 0.5930546 0.2102384 0.04243491 0.0885656
8      FALSE     0.0   2.50 0.5930546 0.2102384 0.04243491 0.0885656
49      TRUE     0.0   0.75       NaN       NaN         NA        NA
50      TRUE     0.0   1.00       NaN       NaN         NA        NA
51      TRUE     0.0   1.25       NaN       NaN         NA        NA
52      TRUE     0.0   1.50       NaN       NaN         NA        NA
53      TRUE     0.0   1.75       NaN       NaN         NA        NA
54      TRUE     0.0   2.00       NaN       NaN         NA        NA
55      TRUE     0.0   2.25       NaN       NaN         NA        NA
56      TRUE     0.0   2.50       NaN       NaN         NA        NA
9      FALSE     0.5   0.75 0.5930546 0.2102384 0.04243491 0.0885656
10     FALSE     0.5   1.00 0.5930546 0.2102384 0.04243491 0.0885656
11     FALSE     0.5   1.25 0.5930546 0.2102384 0.04243491 0.0885656
12     FALSE     0.5   1.50 0.5930546 0.2102384 0.04243491 0.0885656
13     FALSE     0.5   1.75 0.5930546 0.2102384 0.04243491 0.0885656
14     FALSE     0.5   2.00 0.5930546 0.2102384 0.04243491 0.0885656
15     FALSE     0.5   2.25 0.5930546 0.2102384 0.04243491 0.0885656
16     FALSE     0.5   2.50 0.5930546 0.2102384 0.04243491 0.0885656
57      TRUE     0.5   0.75       NaN       NaN         NA        NA
58      TRUE     0.5   1.00       NaN       NaN         NA        NA
59      TRUE     0.5   1.25       NaN       NaN         NA        NA
60      TRUE     0.5   1.50       NaN       NaN         NA        NA
61      TRUE     0.5   1.75       NaN       NaN         NA        NA
62      TRUE     0.5   2.00       NaN       NaN         NA        NA
63      TRUE     0.5   2.25       NaN       NaN         NA        NA
64      TRUE     0.5   2.50       NaN       NaN         NA        NA
17     FALSE     1.0   0.75 0.5930546 0.2102384 0.04243491 0.0885656
18     FALSE     1.0   1.00 0.5930546 0.2102384 0.04243491 0.0885656
19     FALSE     1.0   1.25 0.5930546 0.2102384 0.04243491 0.0885656
20     FALSE     1.0   1.50 0.5930546 0.2102384 0.04243491 0.0885656
21     FALSE     1.0   1.75 0.5930546 0.2102384 0.04243491 0.0885656
22     FALSE     1.0   2.00 0.5930546 0.2102384 0.04243491 0.0885656
23     FALSE     1.0   2.25 0.5930546 0.2102384 0.04243491 0.0885656
24     FALSE     1.0   2.50 0.5930546 0.2102384 0.04243491 0.0885656
65      TRUE     1.0   0.75       NaN       NaN         NA        NA
66      TRUE     1.0   1.00       NaN       NaN         NA        NA
67      TRUE     1.0   1.25       NaN       NaN         NA        NA
68      TRUE     1.0   1.50       NaN       NaN         NA        NA
69      TRUE     1.0   1.75       NaN       NaN         NA        NA
70      TRUE     1.0   2.00       NaN       NaN         NA        NA
71      TRUE     1.0   2.25       NaN       NaN         NA        NA
72      TRUE     1.0   2.50       NaN       NaN         NA        NA
25     FALSE     2.0   0.75 0.5930546 0.2102384 0.04243491 0.0885656
26     FALSE     2.0   1.00 0.5930546 0.2102384 0.04243491 0.0885656
27     FALSE     2.0   1.25 0.5930546 0.2102384 0.04243491 0.0885656
28     FALSE     2.0   1.50 0.5930546 0.2102384 0.04243491 0.0885656
29     FALSE     2.0   1.75 0.5930546 0.2102384 0.04243491 0.0885656
30     FALSE     2.0   2.00 0.5930546 0.2102384 0.04243491 0.0885656
31     FALSE     2.0   2.25 0.5930546 0.2102384 0.04243491 0.0885656
32     FALSE     2.0   2.50 0.5930546 0.2102384 0.04243491 0.0885656
73      TRUE     2.0   0.75       NaN       NaN         NA        NA
74      TRUE     2.0   1.00       NaN       NaN         NA        NA
75      TRUE     2.0   1.25       NaN       NaN         NA        NA
76      TRUE     2.0   1.50       NaN       NaN         NA        NA
77      TRUE     2.0   1.75       NaN       NaN         NA        NA
78      TRUE     2.0   2.00       NaN       NaN         NA        NA
79      TRUE     2.0   2.25       NaN       NaN         NA        NA
80      TRUE     2.0   2.50       NaN       NaN         NA        NA
33     FALSE     3.0   0.75 0.5930546 0.2102384 0.04243491 0.0885656
34     FALSE     3.0   1.00 0.5930546 0.2102384 0.04243491 0.0885656
35     FALSE     3.0   1.25 0.5930546 0.2102384 0.04243491 0.0885656
36     FALSE     3.0   1.50 0.5930546 0.2102384 0.04243491 0.0885656
37     FALSE     3.0   1.75 0.5930546 0.2102384 0.04243491 0.0885656
38     FALSE     3.0   2.00 0.5930546 0.2102384 0.04243491 0.0885656
39     FALSE     3.0   2.25 0.5930546 0.2102384 0.04243491 0.0885656
40     FALSE     3.0   2.50 0.5930546 0.2102384 0.04243491 0.0885656
81      TRUE     3.0   0.75       NaN       NaN         NA        NA
82      TRUE     3.0   1.00       NaN       NaN         NA        NA
83      TRUE     3.0   1.25       NaN       NaN         NA        NA
84      TRUE     3.0   1.50       NaN       NaN         NA        NA
85      TRUE     3.0   1.75       NaN       NaN         NA        NA
86      TRUE     3.0   2.00       NaN       NaN         NA        NA
87      TRUE     3.0   2.25       NaN       NaN         NA        NA
88      TRUE     3.0   2.50       NaN       NaN         NA        NA
41     FALSE     4.0   0.75 0.5930546 0.2102384 0.04243491 0.0885656
42     FALSE     4.0   1.00 0.5930546 0.2102384 0.04243491 0.0885656
43     FALSE     4.0   1.25 0.5930546 0.2102384 0.04243491 0.0885656
44     FALSE     4.0   1.50 0.5930546 0.2102384 0.04243491 0.0885656
45     FALSE     4.0   1.75 0.5930546 0.2102384 0.04243491 0.0885656
46     FALSE     4.0   2.00 0.5930546 0.2102384 0.04243491 0.0885656
47     FALSE     4.0   2.25 0.5930546 0.2102384 0.04243491 0.0885656
48     FALSE     4.0   2.50 0.5930546 0.2102384 0.04243491 0.0885656
89      TRUE     4.0   0.75       NaN       NaN         NA        NA
90      TRUE     4.0   1.00       NaN       NaN         NA        NA
91      TRUE     4.0   1.25       NaN       NaN         NA        NA
92      TRUE     4.0   1.50       NaN       NaN         NA        NA
93      TRUE     4.0   1.75       NaN       NaN         NA        NA
94      TRUE     4.0   2.00       NaN       NaN         NA        NA
95      TRUE     4.0   2.25       NaN       NaN         NA        NA
96      TRUE     4.0   2.50       NaN       NaN         NA        NA
[/C][/ROW]
[ROW][C]> naive_bayes_via_caret$finalModel$tuneValue
  laplace usekernel adjust
1       0     FALSE   0.75
[/C][/ROW]
[ROW][C]> print.naive_bayes(naive_bayes_via_caret$finalModel)
================================== Naive Bayes ================================== 
 
 Call: 
naive_bayes.default(x = x, y = y, laplace = param$laplace, usekernel = FALSE, 
    usepoisson = TRUE, kernel = ..1, bw = "SJ")
--------------------------------------------------------------------------------- 
 
Laplace smoothing: 0
--------------------------------------------------------------------------------- 
 
 A priori probabilities: 
        0         1 
0.4554455 0.5445545 
--------------------------------------------------------------------------------- 
 
 Tables: 
--------------------------------------------------------------------------------- 
 ::: bloodpressureNum (Gaussian) 
--------------------------------------------------------------------------------- 
                
bloodpressureNum         0         1
            mean 134.39855 129.30303
            sd    18.72994  16.16961
--------------------------------------------------------------------------------- 
 ::: sexLabel'Male' (Gaussian) 
--------------------------------------------------------------------------------- 
              
sexLabel'Male'         0         1
          mean 0.8260870 0.5636364
          sd   0.3804155 0.4974436
---------------------------------------------------------------------------------
[/C][/ROW]
[ROW][C]> z
    target bloodpressureNum sexLabel         0          1
1        1              145   'Male' 0.4963321 0.50366788
2        1              130   'Male' 0.4128181 0.58718191
3        1              130 'Female' 0.4128181 0.58718191
4        1              120   'Male' 0.3880164 0.61198357
5        1              120 'Female' 0.3880164 0.61198357
6        1              140   'Male' 0.4621778 0.53782224
7        1              140 'Female' 0.4621778 0.53782224
8        1              120   'Male' 0.3880164 0.61198357
9        1              172   'Male' 0.7586834 0.24131665
10       1              150   'Male' 0.5365819 0.46341807
11       1              140   'Male' 0.4621778 0.53782224
12       1              130 'Female' 0.4128181 0.58718191
13       1              130   'Male' 0.4128181 0.58718191
14       1              110   'Male' 0.3866137 0.61338629
15       1              150 'Female' 0.5365819 0.46341807
16       1              120 'Female' 0.3880164 0.61198357
17       1              120 'Female' 0.3880164 0.61198357
18       1              150 'Female' 0.5365819 0.46341807
19       1              150   'Male' 0.5365819 0.46341807
20       1              140 'Female' 0.4621778 0.53782224
21       1              135   'Male' 0.4343486 0.56565138
22       1              130   'Male' 0.4128181 0.58718191
23       1              140   'Male' 0.4621778 0.53782224
24       1              150   'Male' 0.5365819 0.46341807
25       1              140   'Male' 0.4621778 0.53782224
26       1              160 'Female' 0.6323187 0.36768134
27       1              150   'Male' 0.5365819 0.46341807
28       1              110   'Male' 0.3866137 0.61338629
29       1              140 'Female' 0.4621778 0.53782224
30       1              130   'Male' 0.4128181 0.58718191
31       1              105 'Female' 0.3946059 0.60539414
32       1              120   'Male' 0.3880164 0.61198357
33       1              130   'Male' 0.4128181 0.58718191
34       1              125   'Male' 0.3974336 0.60256642
35       1              125   'Male' 0.3974336 0.60256642
36       1              142 'Female' 0.4750861 0.52491395
37       1              135 'Female' 0.4343486 0.56565138
38       1              150   'Male' 0.5365819 0.46341807
39       1              155 'Female' 0.5822960 0.41770400
40       1              160 'Female' 0.6323187 0.36768134
41       1              140 'Female' 0.4621778 0.53782224
42       1              130   'Male' 0.4128181 0.58718191
43       1              104   'Male' 0.3969106 0.60308940
44       1              130 'Female' 0.4128181 0.58718191
45       1              140   'Male' 0.4621778 0.53782224
46       1              120   'Male' 0.3880164 0.61198357
47       1              140   'Male' 0.4621778 0.53782224
48       1              138   'Male' 0.4502844 0.54971565
49       1              128 'Female' 0.4059371 0.59406295
50       1              138 'Female' 0.4502844 0.54971565
51       1              130 'Female' 0.4128181 0.58718191
52       1              120   'Male' 0.3880164 0.61198357
53       1              130   'Male' 0.4128181 0.58718191
54       1              108 'Female' 0.3891086 0.61089142
55       1              135 'Female' 0.4343486 0.56565138
56       1              134   'Male' 0.4295424 0.57045757
57       1              122   'Male' 0.3910775 0.60892250
58       1              115   'Male' 0.3844291 0.61557088
59       1              118   'Male' 0.3858872 0.61411279
60       1              128 'Female' 0.4059371 0.59406295
61       1              110 'Female' 0.3866137 0.61338629
62       1              108   'Male' 0.3891086 0.61089142
63       1              118   'Male' 0.3858872 0.61411279
64       1              135   'Male' 0.4343486 0.56565138
65       1              140   'Male' 0.4621778 0.53782224
66       1              138 'Female' 0.4502844 0.54971565
67       1              100   'Male' 0.4085263 0.59147370
68       1              130 'Female' 0.4128181 0.58718191
69       1              120   'Male' 0.3880164 0.61198357
70       1              124 'Female' 0.3950781 0.60492192
71       1              120   'Male' 0.3880164 0.61198357
72       1               94   'Male' 0.4333012 0.56669885
73       1              130   'Male' 0.4128181 0.58718191
74       1              140   'Male' 0.4621778 0.53782224
75       1              122 'Female' 0.3910775 0.60892250
76       1              135 'Female' 0.4343486 0.56565138
77       1              125   'Male' 0.3974336 0.60256642
78       1              140   'Male' 0.4621778 0.53782224
79       1              128   'Male' 0.4059371 0.59406295
80       1              105   'Male' 0.3946059 0.60539414
81       1              112   'Male' 0.3850471 0.61495293
82       1              128   'Male' 0.4059371 0.59406295
83       1              102 'Female' 0.4022362 0.59776376
84       1              152   'Male' 0.5542638 0.44573617
85       1              102 'Female' 0.4022362 0.59776376
86       1              115 'Female' 0.3844291 0.61557088
87       1              118   'Male' 0.3858872 0.61411279
88       1              101   'Male' 0.4052600 0.59474002
89       1              110 'Female' 0.3866137 0.61338629
90       1              100 'Female' 0.4085263 0.59147370
91       1              124   'Male' 0.3950781 0.60492192
92       1              132   'Male' 0.4206827 0.57931727
93       1              138   'Male' 0.4502844 0.54971565
94       1              132 'Female' 0.4206827 0.57931727
95       1              112 'Female' 0.3850471 0.61495293
96       1              142   'Male' 0.4750861 0.52491395
97       1              140 'Female' 0.4621778 0.53782224
98       1              108   'Male' 0.3891086 0.61089142
99       1              130   'Male' 0.4128181 0.58718191
100      1              130   'Male' 0.4128181 0.58718191
101      1              148   'Male' 0.5197803 0.48021970
102      1              178   'Male' 0.8175342 0.18246584
103      1              140 'Female' 0.4621778 0.53782224
104      1              120   'Male' 0.3880164 0.61198357
105      1              129   'Male' 0.4092554 0.59074463
106      1              120 'Female' 0.3880164 0.61198357
107      1              160   'Male' 0.6323187 0.36768134
108      1              138 'Female' 0.4502844 0.54971565
109      1              120 'Female' 0.3880164 0.61198357
110      1              110 'Female' 0.3866137 0.61338629
111      1              180 'Female' 0.8355671 0.16443288
112      1              150   'Male' 0.5365819 0.46341807
113      1              140 'Female' 0.4621778 0.53782224
114      1              110   'Male' 0.3866137 0.61338629
115      1              130   'Male' 0.4128181 0.58718191
116      1              120 'Female' 0.3880164 0.61198357
117      1              130   'Male' 0.4128181 0.58718191
118      1              120   'Male' 0.3880164 0.61198357
119      1              105 'Female' 0.3946059 0.60539414
120      1              138 'Female' 0.4502844 0.54971565
121      1              130 'Female' 0.4128181 0.58718191
122      1              138   'Male' 0.4502844 0.54971565
123      1              112 'Female' 0.3850471 0.61495293
124      1              108 'Female' 0.3891086 0.61089142
125      1               94 'Female' 0.4333012 0.56669885
126      1              118 'Female' 0.3858872 0.61411279
127      1              112   'Male' 0.3850471 0.61495293
128      1              152 'Female' 0.5542638 0.44573617
129      1              136 'Female' 0.4394072 0.56059285
130      1              120 'Female' 0.3880164 0.61198357
131      1              160 'Female' 0.6323187 0.36768134
132      1              134 'Female' 0.4295424 0.57045757
133      1              120   'Male' 0.3880164 0.61198357
134      1              110   'Male' 0.3866137 0.61338629
135      1              126 'Female' 0.4000276 0.59997237
136      1              130 'Female' 0.4128181 0.58718191
137      1              120 'Female' 0.3880164 0.61198357
138      1              128   'Male' 0.4059371 0.59406295
139      1              110   'Male' 0.3866137 0.61338629
140      1              128   'Male' 0.4059371 0.59406295
141      1              120 'Female' 0.3880164 0.61198357
142      1              115   'Male' 0.3844291 0.61557088
143      1              120 'Female' 0.3880164 0.61198357
144      1              106 'Female' 0.3925381 0.60746193
145      1              140 'Female' 0.4621778 0.53782224
146      1              156   'Male' 0.5920030 0.40799705
147      1              118 'Female' 0.3858872 0.61411279
148      1              150 'Female' 0.5365819 0.46341807
149      1              120   'Male' 0.3880164 0.61198357
150      1              130   'Male' 0.4128181 0.58718191
151      1              160   'Male' 0.6323187 0.36768134
152      1              112 'Female' 0.3850471 0.61495293
153      1              170   'Male' 0.7379195 0.26208050
154      1              146 'Female' 0.5039079 0.49609205
155      1              138 'Female' 0.4502844 0.54971565
156      1              130 'Female' 0.4128181 0.58718191
157      1              130   'Male' 0.4128181 0.58718191
158      1              122   'Male' 0.3910775 0.60892250
159      1              125   'Male' 0.3974336 0.60256642
160      1              130   'Male' 0.4128181 0.58718191
161      1              120   'Male' 0.3880164 0.61198357
162      1              132 'Female' 0.4206827 0.57931727
163      1              120   'Male' 0.3880164 0.61198357
164      1              138   'Male' 0.4502844 0.54971565
165      1              138   'Male' 0.4502844 0.54971565
166      0              160   'Male' 0.6323187 0.36768134
167      0              120   'Male' 0.3880164 0.61198357
168      0              140 'Female' 0.4621778 0.53782224
169      0              130   'Male' 0.4128181 0.58718191
170      0              140   'Male' 0.4621778 0.53782224
171      0              130   'Male' 0.4128181 0.58718191
172      0              110   'Male' 0.3866137 0.61338629
173      0              120   'Male' 0.3880164 0.61198357
174      0              132   'Male' 0.4206827 0.57931727
175      0              130   'Male' 0.4128181 0.58718191
176      0              110   'Male' 0.3866137 0.61338629
177      0              117   'Male' 0.3851702 0.61482978
178      0              140   'Male' 0.4621778 0.53782224
179      0              120   'Male' 0.3880164 0.61198357
180      0              150   'Male' 0.5365819 0.46341807
181      0              132   'Male' 0.4206827 0.57931727
182      0              150 'Female' 0.5365819 0.46341807
183      0              130 'Female' 0.4128181 0.58718191
184      0              112   'Male' 0.3850471 0.61495293
185      0              150   'Male' 0.5365819 0.46341807
186      0              112   'Male' 0.3850471 0.61495293
187      0              130   'Male' 0.4128181 0.58718191
188      0              124   'Male' 0.3950781 0.60492192
189      0              140   'Male' 0.4621778 0.53782224
190      0              110   'Male' 0.3866137 0.61338629
191      0              130 'Female' 0.4128181 0.58718191
192      0              128   'Male' 0.4059371 0.59406295
193      0              120   'Male' 0.3880164 0.61198357
194      0              145   'Male' 0.4963321 0.50366788
195      0              140   'Male' 0.4621778 0.53782224
196      0              170   'Male' 0.7379195 0.26208050
197      0              150   'Male' 0.5365819 0.46341807
198      0              125   'Male' 0.3974336 0.60256642
199      0              120   'Male' 0.3880164 0.61198357
200      0              110   'Male' 0.3866137 0.61338629
201      0              110   'Male' 0.3866137 0.61338629
202      0              125   'Male' 0.3974336 0.60256642
203      0              150   'Male' 0.5365819 0.46341807
204      0              180   'Male' 0.8355671 0.16443288
205      0              160 'Female' 0.6323187 0.36768134
206      0              128   'Male' 0.4059371 0.59406295
207      0              110   'Male' 0.3866137 0.61338629
208      0              150 'Female' 0.5365819 0.46341807
209      0              120   'Male' 0.3880164 0.61198357
210      0              140   'Male' 0.4621778 0.53782224
211      0              128   'Male' 0.4059371 0.59406295
212      0              120   'Male' 0.3880164 0.61198357
213      0              118   'Male' 0.3858872 0.61411279
214      0              145 'Female' 0.4963321 0.50366788
215      0              125   'Male' 0.3974336 0.60256642
216      0              132 'Female' 0.4206827 0.57931727
217      0              130 'Female' 0.4128181 0.58718191
218      0              130   'Male' 0.4128181 0.58718191
219      0              135   'Male' 0.4343486 0.56565138
220      0              130   'Male' 0.4128181 0.58718191
221      0              150 'Female' 0.5365819 0.46341807
222      0              140   'Male' 0.4621778 0.53782224
223      0              138   'Male' 0.4502844 0.54971565
224      0              200 'Female' 0.9568397 0.04316026
225      0              110   'Male' 0.3866137 0.61338629
226      0              145   'Male' 0.4963321 0.50366788
227      0              120   'Male' 0.3880164 0.61198357
228      0              120   'Male' 0.3880164 0.61198357
229      0              170   'Male' 0.7379195 0.26208050
230      0              125   'Male' 0.3974336 0.60256642
231      0              108   'Male' 0.3891086 0.61089142
232      0              165   'Male' 0.6849258 0.31507416
233      0              160   'Male' 0.6323187 0.36768134
234      0              120   'Male' 0.3880164 0.61198357
235      0              130   'Male' 0.4128181 0.58718191
236      0              140   'Male' 0.4621778 0.53782224
237      0              125   'Male' 0.3974336 0.60256642
238      0              140   'Male' 0.4621778 0.53782224
239      0              125   'Male' 0.3974336 0.60256642
240      0              126   'Male' 0.4000276 0.59997237
241      0              160   'Male' 0.6323187 0.36768134
242      0              174 'Female' 0.7789681 0.22103194
243      0              145   'Male' 0.4963321 0.50366788
244      0              152   'Male' 0.5542638 0.44573617
245      0              132   'Male' 0.4206827 0.57931727
246      0              124   'Male' 0.3950781 0.60492192
247      0              134 'Female' 0.4295424 0.57045757
248      0              160   'Male' 0.6323187 0.36768134
249      0              192   'Male' 0.9214861 0.07851393
250      0              140   'Male' 0.4621778 0.53782224
251      0              140   'Male' 0.4621778 0.53782224
252      0              132   'Male' 0.4206827 0.57931727
253      0              138 'Female' 0.4502844 0.54971565
254      0              100   'Male' 0.4085263 0.59147370
255      0              160   'Male' 0.6323187 0.36768134
256      0              142   'Male' 0.4750861 0.52491395
257      0              128   'Male' 0.4059371 0.59406295
258      0              144   'Male' 0.4890014 0.51099861
259      0              150 'Female' 0.5365819 0.46341807
260      0              120   'Male' 0.3880164 0.61198357
261      0              178 'Female' 0.8175342 0.18246584
262      0              112   'Male' 0.3850471 0.61495293
263      0              123   'Male' 0.3929598 0.60704020
264      0              108 'Female' 0.3891086 0.61089142
265      0              110   'Male' 0.3866137 0.61338629
266      0              112   'Male' 0.3850471 0.61495293
267      0              180 'Female' 0.8355671 0.16443288
268      0              118   'Male' 0.3858872 0.61411279
269      0              122   'Male' 0.3910775 0.60892250
270      0              130   'Male' 0.4128181 0.58718191
271      0              120   'Male' 0.3880164 0.61198357
272      0              134   'Male' 0.4295424 0.57045757
273      0              120   'Male' 0.3880164 0.61198357
274      0              100   'Male' 0.4085263 0.59147370
275      0              110   'Male' 0.3866137 0.61338629
276      0              125   'Male' 0.3974336 0.60256642
277      0              146   'Male' 0.5039079 0.49609205
278      0              124   'Male' 0.3950781 0.60492192
279      0              136 'Female' 0.4394072 0.56059285
280      0              138   'Male' 0.4502844 0.54971565
281      0              136   'Male' 0.4394072 0.56059285
282      0              128   'Male' 0.4059371 0.59406295
283      0              126   'Male' 0.4000276 0.59997237
284      0              152   'Male' 0.5542638 0.44573617
285      0              140   'Male' 0.4621778 0.53782224
286      0              140   'Male' 0.4621778 0.53782224
287      0              134   'Male' 0.4295424 0.57045757
288      0              154   'Male' 0.5727633 0.42723673
289      0              110   'Male' 0.3866137 0.61338629
290      0              128 'Female' 0.4059371 0.59406295
291      0              148   'Male' 0.5197803 0.48021970
292      0              114   'Male' 0.3844045 0.61559550
293      0              170 'Female' 0.7379195 0.26208050
294      0              152   'Male' 0.5542638 0.44573617
295      0              120   'Male' 0.3880164 0.61198357
296      0              140   'Male' 0.4621778 0.53782224
297      0              124 'Female' 0.3950781 0.60492192
298      0              164   'Male' 0.6742979 0.32570206
299      0              140 'Female' 0.4621778 0.53782224
300      0              110   'Male' 0.3866137 0.61338629
301      0              144   'Male' 0.4890014 0.51099861
302      0              130   'Male' 0.4128181 0.58718191
303      0              130 'Female' 0.4128181 0.58718191
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319361&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319361&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Naive Bayes Classifier
> naive_bayes_via_caret$results usekernel laplace adjust Accuracy Kappa AccuracySD KappaSD 1 FALSE 0.0 0.75 0.5930546 0.2102384 0.04243491 0.0885656 2 FALSE 0.0 1.00 0.5930546 0.2102384 0.04243491 0.0885656 3 FALSE 0.0 1.25 0.5930546 0.2102384 0.04243491 0.0885656 4 FALSE 0.0 1.50 0.5930546 0.2102384 0.04243491 0.0885656 5 FALSE 0.0 1.75 0.5930546 0.2102384 0.04243491 0.0885656 6 FALSE 0.0 2.00 0.5930546 0.2102384 0.04243491 0.0885656 7 FALSE 0.0 2.25 0.5930546 0.2102384 0.04243491 0.0885656 8 FALSE 0.0 2.50 0.5930546 0.2102384 0.04243491 0.0885656 49 TRUE 0.0 0.75 NaN NaN NA NA 50 TRUE 0.0 1.00 NaN NaN NA NA 51 TRUE 0.0 1.25 NaN NaN NA NA 52 TRUE 0.0 1.50 NaN NaN NA NA 53 TRUE 0.0 1.75 NaN NaN NA NA 54 TRUE 0.0 2.00 NaN NaN NA NA 55 TRUE 0.0 2.25 NaN NaN NA NA 56 TRUE 0.0 2.50 NaN NaN NA NA 9 FALSE 0.5 0.75 0.5930546 0.2102384 0.04243491 0.0885656 10 FALSE 0.5 1.00 0.5930546 0.2102384 0.04243491 0.0885656 11 FALSE 0.5 1.25 0.5930546 0.2102384 0.04243491 0.0885656 12 FALSE 0.5 1.50 0.5930546 0.2102384 0.04243491 0.0885656 13 FALSE 0.5 1.75 0.5930546 0.2102384 0.04243491 0.0885656 14 FALSE 0.5 2.00 0.5930546 0.2102384 0.04243491 0.0885656 15 FALSE 0.5 2.25 0.5930546 0.2102384 0.04243491 0.0885656 16 FALSE 0.5 2.50 0.5930546 0.2102384 0.04243491 0.0885656 57 TRUE 0.5 0.75 NaN NaN NA NA 58 TRUE 0.5 1.00 NaN NaN NA NA 59 TRUE 0.5 1.25 NaN NaN NA NA 60 TRUE 0.5 1.50 NaN NaN NA NA 61 TRUE 0.5 1.75 NaN NaN NA NA 62 TRUE 0.5 2.00 NaN NaN NA NA 63 TRUE 0.5 2.25 NaN NaN NA NA 64 TRUE 0.5 2.50 NaN NaN NA NA 17 FALSE 1.0 0.75 0.5930546 0.2102384 0.04243491 0.0885656 18 FALSE 1.0 1.00 0.5930546 0.2102384 0.04243491 0.0885656 19 FALSE 1.0 1.25 0.5930546 0.2102384 0.04243491 0.0885656 20 FALSE 1.0 1.50 0.5930546 0.2102384 0.04243491 0.0885656 21 FALSE 1.0 1.75 0.5930546 0.2102384 0.04243491 0.0885656 22 FALSE 1.0 2.00 0.5930546 0.2102384 0.04243491 0.0885656 23 FALSE 1.0 2.25 0.5930546 0.2102384 0.04243491 0.0885656 24 FALSE 1.0 2.50 0.5930546 0.2102384 0.04243491 0.0885656 65 TRUE 1.0 0.75 NaN NaN NA NA 66 TRUE 1.0 1.00 NaN NaN NA NA 67 TRUE 1.0 1.25 NaN NaN NA NA 68 TRUE 1.0 1.50 NaN NaN NA NA 69 TRUE 1.0 1.75 NaN NaN NA NA 70 TRUE 1.0 2.00 NaN NaN NA NA 71 TRUE 1.0 2.25 NaN NaN NA NA 72 TRUE 1.0 2.50 NaN NaN NA NA 25 FALSE 2.0 0.75 0.5930546 0.2102384 0.04243491 0.0885656 26 FALSE 2.0 1.00 0.5930546 0.2102384 0.04243491 0.0885656 27 FALSE 2.0 1.25 0.5930546 0.2102384 0.04243491 0.0885656 28 FALSE 2.0 1.50 0.5930546 0.2102384 0.04243491 0.0885656 29 FALSE 2.0 1.75 0.5930546 0.2102384 0.04243491 0.0885656 30 FALSE 2.0 2.00 0.5930546 0.2102384 0.04243491 0.0885656 31 FALSE 2.0 2.25 0.5930546 0.2102384 0.04243491 0.0885656 32 FALSE 2.0 2.50 0.5930546 0.2102384 0.04243491 0.0885656 73 TRUE 2.0 0.75 NaN NaN NA NA 74 TRUE 2.0 1.00 NaN NaN NA NA 75 TRUE 2.0 1.25 NaN NaN NA NA 76 TRUE 2.0 1.50 NaN NaN NA NA 77 TRUE 2.0 1.75 NaN NaN NA NA 78 TRUE 2.0 2.00 NaN NaN NA NA 79 TRUE 2.0 2.25 NaN NaN NA NA 80 TRUE 2.0 2.50 NaN NaN NA NA 33 FALSE 3.0 0.75 0.5930546 0.2102384 0.04243491 0.0885656 34 FALSE 3.0 1.00 0.5930546 0.2102384 0.04243491 0.0885656 35 FALSE 3.0 1.25 0.5930546 0.2102384 0.04243491 0.0885656 36 FALSE 3.0 1.50 0.5930546 0.2102384 0.04243491 0.0885656 37 FALSE 3.0 1.75 0.5930546 0.2102384 0.04243491 0.0885656 38 FALSE 3.0 2.00 0.5930546 0.2102384 0.04243491 0.0885656 39 FALSE 3.0 2.25 0.5930546 0.2102384 0.04243491 0.0885656 40 FALSE 3.0 2.50 0.5930546 0.2102384 0.04243491 0.0885656 81 TRUE 3.0 0.75 NaN NaN NA NA 82 TRUE 3.0 1.00 NaN NaN NA NA 83 TRUE 3.0 1.25 NaN NaN NA NA 84 TRUE 3.0 1.50 NaN NaN NA NA 85 TRUE 3.0 1.75 NaN NaN NA NA 86 TRUE 3.0 2.00 NaN NaN NA NA 87 TRUE 3.0 2.25 NaN NaN NA NA 88 TRUE 3.0 2.50 NaN NaN NA NA 41 FALSE 4.0 0.75 0.5930546 0.2102384 0.04243491 0.0885656 42 FALSE 4.0 1.00 0.5930546 0.2102384 0.04243491 0.0885656 43 FALSE 4.0 1.25 0.5930546 0.2102384 0.04243491 0.0885656 44 FALSE 4.0 1.50 0.5930546 0.2102384 0.04243491 0.0885656 45 FALSE 4.0 1.75 0.5930546 0.2102384 0.04243491 0.0885656 46 FALSE 4.0 2.00 0.5930546 0.2102384 0.04243491 0.0885656 47 FALSE 4.0 2.25 0.5930546 0.2102384 0.04243491 0.0885656 48 FALSE 4.0 2.50 0.5930546 0.2102384 0.04243491 0.0885656 89 TRUE 4.0 0.75 NaN NaN NA NA 90 TRUE 4.0 1.00 NaN NaN NA NA 91 TRUE 4.0 1.25 NaN NaN NA NA 92 TRUE 4.0 1.50 NaN NaN NA NA 93 TRUE 4.0 1.75 NaN NaN NA NA 94 TRUE 4.0 2.00 NaN NaN NA NA 95 TRUE 4.0 2.25 NaN NaN NA NA 96 TRUE 4.0 2.50 NaN NaN NA NA
> naive_bayes_via_caret$finalModel$tuneValue laplace usekernel adjust 1 0 FALSE 0.75
> print.naive_bayes(naive_bayes_via_caret$finalModel) ================================== Naive Bayes ================================== Call: naive_bayes.default(x = x, y = y, laplace = param$laplace, usekernel = FALSE, usepoisson = TRUE, kernel = ..1, bw = "SJ") --------------------------------------------------------------------------------- Laplace smoothing: 0 --------------------------------------------------------------------------------- A priori probabilities: 0 1 0.4554455 0.5445545 --------------------------------------------------------------------------------- Tables: --------------------------------------------------------------------------------- ::: bloodpressureNum (Gaussian) --------------------------------------------------------------------------------- bloodpressureNum 0 1 mean 134.39855 129.30303 sd 18.72994 16.16961 --------------------------------------------------------------------------------- ::: sexLabel'Male' (Gaussian) --------------------------------------------------------------------------------- sexLabel'Male' 0 1 mean 0.8260870 0.5636364 sd 0.3804155 0.4974436 ---------------------------------------------------------------------------------
> z target bloodpressureNum sexLabel 0 1 1 1 145 'Male' 0.4963321 0.50366788 2 1 130 'Male' 0.4128181 0.58718191 3 1 130 'Female' 0.4128181 0.58718191 4 1 120 'Male' 0.3880164 0.61198357 5 1 120 'Female' 0.3880164 0.61198357 6 1 140 'Male' 0.4621778 0.53782224 7 1 140 'Female' 0.4621778 0.53782224 8 1 120 'Male' 0.3880164 0.61198357 9 1 172 'Male' 0.7586834 0.24131665 10 1 150 'Male' 0.5365819 0.46341807 11 1 140 'Male' 0.4621778 0.53782224 12 1 130 'Female' 0.4128181 0.58718191 13 1 130 'Male' 0.4128181 0.58718191 14 1 110 'Male' 0.3866137 0.61338629 15 1 150 'Female' 0.5365819 0.46341807 16 1 120 'Female' 0.3880164 0.61198357 17 1 120 'Female' 0.3880164 0.61198357 18 1 150 'Female' 0.5365819 0.46341807 19 1 150 'Male' 0.5365819 0.46341807 20 1 140 'Female' 0.4621778 0.53782224 21 1 135 'Male' 0.4343486 0.56565138 22 1 130 'Male' 0.4128181 0.58718191 23 1 140 'Male' 0.4621778 0.53782224 24 1 150 'Male' 0.5365819 0.46341807 25 1 140 'Male' 0.4621778 0.53782224 26 1 160 'Female' 0.6323187 0.36768134 27 1 150 'Male' 0.5365819 0.46341807 28 1 110 'Male' 0.3866137 0.61338629 29 1 140 'Female' 0.4621778 0.53782224 30 1 130 'Male' 0.4128181 0.58718191 31 1 105 'Female' 0.3946059 0.60539414 32 1 120 'Male' 0.3880164 0.61198357 33 1 130 'Male' 0.4128181 0.58718191 34 1 125 'Male' 0.3974336 0.60256642 35 1 125 'Male' 0.3974336 0.60256642 36 1 142 'Female' 0.4750861 0.52491395 37 1 135 'Female' 0.4343486 0.56565138 38 1 150 'Male' 0.5365819 0.46341807 39 1 155 'Female' 0.5822960 0.41770400 40 1 160 'Female' 0.6323187 0.36768134 41 1 140 'Female' 0.4621778 0.53782224 42 1 130 'Male' 0.4128181 0.58718191 43 1 104 'Male' 0.3969106 0.60308940 44 1 130 'Female' 0.4128181 0.58718191 45 1 140 'Male' 0.4621778 0.53782224 46 1 120 'Male' 0.3880164 0.61198357 47 1 140 'Male' 0.4621778 0.53782224 48 1 138 'Male' 0.4502844 0.54971565 49 1 128 'Female' 0.4059371 0.59406295 50 1 138 'Female' 0.4502844 0.54971565 51 1 130 'Female' 0.4128181 0.58718191 52 1 120 'Male' 0.3880164 0.61198357 53 1 130 'Male' 0.4128181 0.58718191 54 1 108 'Female' 0.3891086 0.61089142 55 1 135 'Female' 0.4343486 0.56565138 56 1 134 'Male' 0.4295424 0.57045757 57 1 122 'Male' 0.3910775 0.60892250 58 1 115 'Male' 0.3844291 0.61557088 59 1 118 'Male' 0.3858872 0.61411279 60 1 128 'Female' 0.4059371 0.59406295 61 1 110 'Female' 0.3866137 0.61338629 62 1 108 'Male' 0.3891086 0.61089142 63 1 118 'Male' 0.3858872 0.61411279 64 1 135 'Male' 0.4343486 0.56565138 65 1 140 'Male' 0.4621778 0.53782224 66 1 138 'Female' 0.4502844 0.54971565 67 1 100 'Male' 0.4085263 0.59147370 68 1 130 'Female' 0.4128181 0.58718191 69 1 120 'Male' 0.3880164 0.61198357 70 1 124 'Female' 0.3950781 0.60492192 71 1 120 'Male' 0.3880164 0.61198357 72 1 94 'Male' 0.4333012 0.56669885 73 1 130 'Male' 0.4128181 0.58718191 74 1 140 'Male' 0.4621778 0.53782224 75 1 122 'Female' 0.3910775 0.60892250 76 1 135 'Female' 0.4343486 0.56565138 77 1 125 'Male' 0.3974336 0.60256642 78 1 140 'Male' 0.4621778 0.53782224 79 1 128 'Male' 0.4059371 0.59406295 80 1 105 'Male' 0.3946059 0.60539414 81 1 112 'Male' 0.3850471 0.61495293 82 1 128 'Male' 0.4059371 0.59406295 83 1 102 'Female' 0.4022362 0.59776376 84 1 152 'Male' 0.5542638 0.44573617 85 1 102 'Female' 0.4022362 0.59776376 86 1 115 'Female' 0.3844291 0.61557088 87 1 118 'Male' 0.3858872 0.61411279 88 1 101 'Male' 0.4052600 0.59474002 89 1 110 'Female' 0.3866137 0.61338629 90 1 100 'Female' 0.4085263 0.59147370 91 1 124 'Male' 0.3950781 0.60492192 92 1 132 'Male' 0.4206827 0.57931727 93 1 138 'Male' 0.4502844 0.54971565 94 1 132 'Female' 0.4206827 0.57931727 95 1 112 'Female' 0.3850471 0.61495293 96 1 142 'Male' 0.4750861 0.52491395 97 1 140 'Female' 0.4621778 0.53782224 98 1 108 'Male' 0.3891086 0.61089142 99 1 130 'Male' 0.4128181 0.58718191 100 1 130 'Male' 0.4128181 0.58718191 101 1 148 'Male' 0.5197803 0.48021970 102 1 178 'Male' 0.8175342 0.18246584 103 1 140 'Female' 0.4621778 0.53782224 104 1 120 'Male' 0.3880164 0.61198357 105 1 129 'Male' 0.4092554 0.59074463 106 1 120 'Female' 0.3880164 0.61198357 107 1 160 'Male' 0.6323187 0.36768134 108 1 138 'Female' 0.4502844 0.54971565 109 1 120 'Female' 0.3880164 0.61198357 110 1 110 'Female' 0.3866137 0.61338629 111 1 180 'Female' 0.8355671 0.16443288 112 1 150 'Male' 0.5365819 0.46341807 113 1 140 'Female' 0.4621778 0.53782224 114 1 110 'Male' 0.3866137 0.61338629 115 1 130 'Male' 0.4128181 0.58718191 116 1 120 'Female' 0.3880164 0.61198357 117 1 130 'Male' 0.4128181 0.58718191 118 1 120 'Male' 0.3880164 0.61198357 119 1 105 'Female' 0.3946059 0.60539414 120 1 138 'Female' 0.4502844 0.54971565 121 1 130 'Female' 0.4128181 0.58718191 122 1 138 'Male' 0.4502844 0.54971565 123 1 112 'Female' 0.3850471 0.61495293 124 1 108 'Female' 0.3891086 0.61089142 125 1 94 'Female' 0.4333012 0.56669885 126 1 118 'Female' 0.3858872 0.61411279 127 1 112 'Male' 0.3850471 0.61495293 128 1 152 'Female' 0.5542638 0.44573617 129 1 136 'Female' 0.4394072 0.56059285 130 1 120 'Female' 0.3880164 0.61198357 131 1 160 'Female' 0.6323187 0.36768134 132 1 134 'Female' 0.4295424 0.57045757 133 1 120 'Male' 0.3880164 0.61198357 134 1 110 'Male' 0.3866137 0.61338629 135 1 126 'Female' 0.4000276 0.59997237 136 1 130 'Female' 0.4128181 0.58718191 137 1 120 'Female' 0.3880164 0.61198357 138 1 128 'Male' 0.4059371 0.59406295 139 1 110 'Male' 0.3866137 0.61338629 140 1 128 'Male' 0.4059371 0.59406295 141 1 120 'Female' 0.3880164 0.61198357 142 1 115 'Male' 0.3844291 0.61557088 143 1 120 'Female' 0.3880164 0.61198357 144 1 106 'Female' 0.3925381 0.60746193 145 1 140 'Female' 0.4621778 0.53782224 146 1 156 'Male' 0.5920030 0.40799705 147 1 118 'Female' 0.3858872 0.61411279 148 1 150 'Female' 0.5365819 0.46341807 149 1 120 'Male' 0.3880164 0.61198357 150 1 130 'Male' 0.4128181 0.58718191 151 1 160 'Male' 0.6323187 0.36768134 152 1 112 'Female' 0.3850471 0.61495293 153 1 170 'Male' 0.7379195 0.26208050 154 1 146 'Female' 0.5039079 0.49609205 155 1 138 'Female' 0.4502844 0.54971565 156 1 130 'Female' 0.4128181 0.58718191 157 1 130 'Male' 0.4128181 0.58718191 158 1 122 'Male' 0.3910775 0.60892250 159 1 125 'Male' 0.3974336 0.60256642 160 1 130 'Male' 0.4128181 0.58718191 161 1 120 'Male' 0.3880164 0.61198357 162 1 132 'Female' 0.4206827 0.57931727 163 1 120 'Male' 0.3880164 0.61198357 164 1 138 'Male' 0.4502844 0.54971565 165 1 138 'Male' 0.4502844 0.54971565 166 0 160 'Male' 0.6323187 0.36768134 167 0 120 'Male' 0.3880164 0.61198357 168 0 140 'Female' 0.4621778 0.53782224 169 0 130 'Male' 0.4128181 0.58718191 170 0 140 'Male' 0.4621778 0.53782224 171 0 130 'Male' 0.4128181 0.58718191 172 0 110 'Male' 0.3866137 0.61338629 173 0 120 'Male' 0.3880164 0.61198357 174 0 132 'Male' 0.4206827 0.57931727 175 0 130 'Male' 0.4128181 0.58718191 176 0 110 'Male' 0.3866137 0.61338629 177 0 117 'Male' 0.3851702 0.61482978 178 0 140 'Male' 0.4621778 0.53782224 179 0 120 'Male' 0.3880164 0.61198357 180 0 150 'Male' 0.5365819 0.46341807 181 0 132 'Male' 0.4206827 0.57931727 182 0 150 'Female' 0.5365819 0.46341807 183 0 130 'Female' 0.4128181 0.58718191 184 0 112 'Male' 0.3850471 0.61495293 185 0 150 'Male' 0.5365819 0.46341807 186 0 112 'Male' 0.3850471 0.61495293 187 0 130 'Male' 0.4128181 0.58718191 188 0 124 'Male' 0.3950781 0.60492192 189 0 140 'Male' 0.4621778 0.53782224 190 0 110 'Male' 0.3866137 0.61338629 191 0 130 'Female' 0.4128181 0.58718191 192 0 128 'Male' 0.4059371 0.59406295 193 0 120 'Male' 0.3880164 0.61198357 194 0 145 'Male' 0.4963321 0.50366788 195 0 140 'Male' 0.4621778 0.53782224 196 0 170 'Male' 0.7379195 0.26208050 197 0 150 'Male' 0.5365819 0.46341807 198 0 125 'Male' 0.3974336 0.60256642 199 0 120 'Male' 0.3880164 0.61198357 200 0 110 'Male' 0.3866137 0.61338629 201 0 110 'Male' 0.3866137 0.61338629 202 0 125 'Male' 0.3974336 0.60256642 203 0 150 'Male' 0.5365819 0.46341807 204 0 180 'Male' 0.8355671 0.16443288 205 0 160 'Female' 0.6323187 0.36768134 206 0 128 'Male' 0.4059371 0.59406295 207 0 110 'Male' 0.3866137 0.61338629 208 0 150 'Female' 0.5365819 0.46341807 209 0 120 'Male' 0.3880164 0.61198357 210 0 140 'Male' 0.4621778 0.53782224 211 0 128 'Male' 0.4059371 0.59406295 212 0 120 'Male' 0.3880164 0.61198357 213 0 118 'Male' 0.3858872 0.61411279 214 0 145 'Female' 0.4963321 0.50366788 215 0 125 'Male' 0.3974336 0.60256642 216 0 132 'Female' 0.4206827 0.57931727 217 0 130 'Female' 0.4128181 0.58718191 218 0 130 'Male' 0.4128181 0.58718191 219 0 135 'Male' 0.4343486 0.56565138 220 0 130 'Male' 0.4128181 0.58718191 221 0 150 'Female' 0.5365819 0.46341807 222 0 140 'Male' 0.4621778 0.53782224 223 0 138 'Male' 0.4502844 0.54971565 224 0 200 'Female' 0.9568397 0.04316026 225 0 110 'Male' 0.3866137 0.61338629 226 0 145 'Male' 0.4963321 0.50366788 227 0 120 'Male' 0.3880164 0.61198357 228 0 120 'Male' 0.3880164 0.61198357 229 0 170 'Male' 0.7379195 0.26208050 230 0 125 'Male' 0.3974336 0.60256642 231 0 108 'Male' 0.3891086 0.61089142 232 0 165 'Male' 0.6849258 0.31507416 233 0 160 'Male' 0.6323187 0.36768134 234 0 120 'Male' 0.3880164 0.61198357 235 0 130 'Male' 0.4128181 0.58718191 236 0 140 'Male' 0.4621778 0.53782224 237 0 125 'Male' 0.3974336 0.60256642 238 0 140 'Male' 0.4621778 0.53782224 239 0 125 'Male' 0.3974336 0.60256642 240 0 126 'Male' 0.4000276 0.59997237 241 0 160 'Male' 0.6323187 0.36768134 242 0 174 'Female' 0.7789681 0.22103194 243 0 145 'Male' 0.4963321 0.50366788 244 0 152 'Male' 0.5542638 0.44573617 245 0 132 'Male' 0.4206827 0.57931727 246 0 124 'Male' 0.3950781 0.60492192 247 0 134 'Female' 0.4295424 0.57045757 248 0 160 'Male' 0.6323187 0.36768134 249 0 192 'Male' 0.9214861 0.07851393 250 0 140 'Male' 0.4621778 0.53782224 251 0 140 'Male' 0.4621778 0.53782224 252 0 132 'Male' 0.4206827 0.57931727 253 0 138 'Female' 0.4502844 0.54971565 254 0 100 'Male' 0.4085263 0.59147370 255 0 160 'Male' 0.6323187 0.36768134 256 0 142 'Male' 0.4750861 0.52491395 257 0 128 'Male' 0.4059371 0.59406295 258 0 144 'Male' 0.4890014 0.51099861 259 0 150 'Female' 0.5365819 0.46341807 260 0 120 'Male' 0.3880164 0.61198357 261 0 178 'Female' 0.8175342 0.18246584 262 0 112 'Male' 0.3850471 0.61495293 263 0 123 'Male' 0.3929598 0.60704020 264 0 108 'Female' 0.3891086 0.61089142 265 0 110 'Male' 0.3866137 0.61338629 266 0 112 'Male' 0.3850471 0.61495293 267 0 180 'Female' 0.8355671 0.16443288 268 0 118 'Male' 0.3858872 0.61411279 269 0 122 'Male' 0.3910775 0.60892250 270 0 130 'Male' 0.4128181 0.58718191 271 0 120 'Male' 0.3880164 0.61198357 272 0 134 'Male' 0.4295424 0.57045757 273 0 120 'Male' 0.3880164 0.61198357 274 0 100 'Male' 0.4085263 0.59147370 275 0 110 'Male' 0.3866137 0.61338629 276 0 125 'Male' 0.3974336 0.60256642 277 0 146 'Male' 0.5039079 0.49609205 278 0 124 'Male' 0.3950781 0.60492192 279 0 136 'Female' 0.4394072 0.56059285 280 0 138 'Male' 0.4502844 0.54971565 281 0 136 'Male' 0.4394072 0.56059285 282 0 128 'Male' 0.4059371 0.59406295 283 0 126 'Male' 0.4000276 0.59997237 284 0 152 'Male' 0.5542638 0.44573617 285 0 140 'Male' 0.4621778 0.53782224 286 0 140 'Male' 0.4621778 0.53782224 287 0 134 'Male' 0.4295424 0.57045757 288 0 154 'Male' 0.5727633 0.42723673 289 0 110 'Male' 0.3866137 0.61338629 290 0 128 'Female' 0.4059371 0.59406295 291 0 148 'Male' 0.5197803 0.48021970 292 0 114 'Male' 0.3844045 0.61559550 293 0 170 'Female' 0.7379195 0.26208050 294 0 152 'Male' 0.5542638 0.44573617 295 0 120 'Male' 0.3880164 0.61198357 296 0 140 'Male' 0.4621778 0.53782224 297 0 124 'Female' 0.3950781 0.60492192 298 0 164 'Male' 0.6742979 0.32570206 299 0 140 'Female' 0.4621778 0.53782224 300 0 110 'Male' 0.3866137 0.61338629 301 0 144 'Male' 0.4890014 0.51099861 302 0 130 'Male' 0.4128181 0.58718191 303 0 130 'Female' 0.4128181 0.58718191

Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = gaussian ; par3 = cnc ; par4 = no ;

R code (references can be found in the software module):

par4 <- 'no'
par3 <- 'cnc'
par2 <- 'gaussian'
par1 <- '3'
.libPaths()
library(caret)
library(naivebayes)
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the target! The first column was selected by default.'
}
print.naive_bayes <- function (x, ...) {
model <- 'Naive Bayes'
n_char <- getOption('width')
str_left_right <- paste0(rep('=', floor((n_char - nchar(model)) / 2)),
collapse = '')
str_full <- paste0(str_left_right, ' ', model, ' ',
ifelse(n_char %% 2 != 0, '=', ''), str_left_right)
len <- nchar(str_full)
l <- paste0(rep('-', len), collapse = '')
cat('\n')
cat(str_full, '\n', '\n', 'Call:', '\n')
print(x$call)
cat('\n')
cat(l, '\n', '\n')
cat( 'Laplace smoothing:', x$laplace)
cat('\n')
cat('\n')
cat(l, '\n', '\n')
cat(' A priori probabilities:', '\n')
print(x$prior)
cat('\n')
cat(l, '\n', '\n')
cat(' Tables:', '\n')
tabs <- x$tables
n <- length(x$tables)
indices <- seq_len(min(25,n))
tabs <- tabs[indices]
print(tabs)
if (n > 25) {
cat('\n\n')
cat('# ... and', n - 25, ifelse(n - 25 == 1, 'more table\n\n', 'more tables\n\n'))
cat(l)
}
cat('\n\n')
}
x <- na.omit(data.frame(t(x)))
k <- length(x[1,])
n <- length(x[,1])
x <- as.data.frame(x)
for(ii in 1:k) {
if(substr(par3,ii,ii) == 'c') x[,ii] <- as.character(x[,ii])
if(substr(par3,ii,ii) == 'n') x[,ii] <- as.numeric(x[,ii])
}
myf <- formula(paste(colnames(x)[par1],' ~ .',sep=''))
myf
nb_grid <-   expand.grid(usekernel = c(TRUE, FALSE), laplace = c(0, 0.5, 1, 2, 3, 4), adjust = c(0.75, 1, 1.25, 1.5, 1.75, 2, 2.25, 2.5))
fitControl <- trainControl(method = 'repeatedcv', number = 10, repeats = 5)
if(par4=='no') {
naive_bayes_via_caret <- train(myf, data = x, method = 'naive_bayes', kernel = par2, bw = 'SJ', usepoisson = TRUE, tuneGrid = nb_grid)
}
if(par4=='yes') {
naive_bayes_via_caret <- train(myf, data = x, method = 'naive_bayes', kernel = par2, bw = 'SJ', usepoisson = TRUE, tuneGrid = nb_grid, trControl = fitControl)
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Naive Bayes Classifier',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('naive_bayes_via_caret$results'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('naive_bayes_via_caret$finalModel$tuneValue'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('',RC.texteval('print.naive_bayes(naive_bayes_via_caret$finalModel)'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
z <- cbind(x, predict(naive_bayes_via_caret$finalModel, x, type = 'prob'))
a<-table.element(a,paste('',RC.texteval('z'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code