“El clasificador ingenuo de Bayes es un clasificador simple que se basa en el conocido teorema de Bayes. A pesar de su simplicidad, siguió siendo una opción popular para la clasificación de textos. Ahora en Naive Bayes, el algoritmo evalúa una probabilidad para cada clase, cuando se dan los valores predictores. E intuitivamente, podemos ir a la clase, que tiene la mayor probabilidad.” (Khan, 2017) El clasificador Naive Bayes aplica el conocido teorema de Bayes para la probabilidad condicional, el cual es la base para la creación del clasificador, y requiere de variables categoricas.
library(naivebayes)## naivebayes 0.9.6 loadedlibrary(caret)## Loading required package: lattice## Loading required package: ggplot2library(ggplot2)
library(e1071)
library(lattice)# LOS VALORES POSITIVOS TOMAN EL VALOR DE 1, MIENTRAS QUE LOS VALORES NEGATIVOS TOMARÁN EL VALOR DE 0.
ep <- read.csv2("DATOS PARA NAIVE BAYES2.csv")
ep## YY X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 P.MAT P.LEN
## 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 4.6 4.8
## 2 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 5.0 5.1
## 3 0 0 1 0 1 1 1 0 0 0 0 1 1 0 0 0 0 4.6 4.5
## 4 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 4.3 4.8
## 5 0 0 0 0 1 1 1 1 0 0 0 1 0 0 0 1 1 6.4 5.8
## 6 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 6.3 6.3
## 7 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 1 5.5 5.9
## 8 0 1 1 1 0 1 0 1 0 0 1 1 1 0 0 1 1 5.0 5.5
## 9 0 0 1 0 1 1 1 1 0 1 1 0 0 0 0 1 1 5.9 5.2
## 10 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 4.4 4.8
## 11 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 5.5 5.9
## 12 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 1 4.5 5.0
## 13 1 1 1 0 1 1 0 1 1 1 1 1 1 0 0 1 1 5.5 4.8
## 14 0 1 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 5.0 5.2
## 15 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 5.1 5.1
## 16 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 4.7 4.3
## 17 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 1 1 4.4 4.8
## 18 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 5.7 5.9
## 19 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.5 6.8
## 20 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.6 6.6
## 21 0 1 1 1 1 1 1 1 0 0 1 0 1 0 0 0 1 5.1 5.5
## 22 0 0 1 1 0 0 0 0 0 1 0 0 1 0 1 1 1 4.2 4.8
## 23 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 6.4 5.9
## 24 1 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 5.4 4.9
## 25 0 1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 1 4.4 5.2
## 26 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 4.1 4.0
## 27 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 6.1 6.2
## 28 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 6.2 6.1
## 29 0 0 0 1 1 1 0 1 0 1 0 1 0 1 0 0 0 4.9 5.1
## 30 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 1 4.2 4.7
## 31 0 0 1 1 0 1 0 1 1 1 1 0 0 0 0 0 1 4.9 5.5
## 32 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 5.4 6.1
## 33 0 1 1 0 0 0 0 1 1 0 0 1 0 0 0 0 0 4.1 4.4
## 34 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 4.1 4.0
## 35 1 1 0 1 1 1 1 1 1 1 1 0 0 1 0 1 1 5.3 5.2
## 36 0 0 1 1 1 1 1 1 0 1 0 1 0 1 0 0 1 5.2 5.1
## 37 0 1 1 0 1 0 1 0 1 0 0 1 1 1 0 1 0 5.3 5.0
## 38 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 0 1 4.0 4.0
## 39 0 0 0 1 1 0 0 1 0 0 1 1 0 0 0 0 1 4.8 4.8
## 40 0 0 0 1 1 0 0 1 1 0 1 1 1 0 0 1 1 5.5 5.4
## 41 1 1 1 0 1 1 0 0 1 1 0 1 0 1 1 1 1 5.2 5.5
## 42 1 1 0 0 1 1 1 1 0 1 1 1 1 1 0 1 1 5.1 5.2
## 43 1 1 1 1 0 1 1 1 0 1 1 0 1 0 0 1 1 5.6 5.1
## 44 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 0 1 4.9 4.7
## 45 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 6.3 6.7
## 46 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 5.3 4.9
## 47 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.5 6.3
## 48 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 5.5 5.4
## 49 0 1 0 1 1 0 1 0 0 0 1 1 0 1 1 1 1 5.2 4.8
## 50 0 0 0 0 1 1 0 1 1 1 1 1 0 0 1 1 1 5.2 5.3
## 51 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 6.2 6.4
## 52 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0 0 1 4.8 5.1
## 53 0 1 0 1 1 1 1 1 0 0 1 0 0 0 1 1 1 5.5 5.1
## 54 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 1 1 5.9 5.9
## 55 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 5.4 5.5
## 56 1 1 0 1 1 0 1 1 1 1 1 0 1 0 1 1 1 5.2 5.0
## 57 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0 1 1 5.3 5.1
## 58 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 5.8 5.0
## 59 0 0 1 0 1 0 1 1 1 1 1 1 1 0 0 0 1 5.6 5.2
## 60 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 5.2 4.7
## 61 0 0 1 0 1 1 0 0 0 1 1 1 1 0 1 1 0 4.9 5.0
## 62 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 4.5 4.4
## 63 0 1 1 0 1 0 0 1 1 1 0 1 1 0 0 0 1 5.1 5.7
## 64 1 0 0 1 1 1 0 1 1 1 1 1 0 1 0 1 1 5.4 5.1
## 65 0 1 1 1 1 0 1 0 0 0 1 0 1 0 1 0 1 5.0 5.2
## 66 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 5.9 5.3
## 67 0 1 1 0 1 1 1 1 0 1 1 1 0 0 1 0 0 5.2 5.2
## 68 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.7 6.8
## 69 0 1 0 1 1 1 0 1 0 0 0 1 1 0 0 1 1 5.2 5.3
## 70 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.8 6.7
## 71 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 6.2 6.4
## 72 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.6 6.5
## 73 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.9 6.6
## 74 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.7 6.5
## 75 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 6.1 6.3
## 76 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 6.4 6.2
## 77 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 5.3 5.9
## 78 0 0 1 1 1 1 0 1 0 1 1 0 0 0 0 1 0 5.3 5.2
## 79 1 1 1 0 1 0 1 1 1 0 1 1 1 1 0 1 1 5.4 4.6
## 80 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 4.8 5.1
## 81 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.8 6.7
## 82 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 6.4 6.1
## 83 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 5.5 4.8
## 84 1 1 1 1 1 0 1 1 1 1 0 1 0 0 1 0 1 4.9 5.9
## 85 0 1 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 5.0 4.7
## 86 1 1 0 1 1 1 1 1 0 0 1 1 1 0 0 1 1 5.5 4.3
## 87 0 0 1 1 0 1 1 1 0 0 0 0 1 0 0 1 1 5.0 4.8
## 88 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.0 6.4
## 89 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 1 0 5.2 4.9
## 90 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 6.3 6.2
## 91 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 0 5.5 4.6
## 92 1 1 1 0 0 1 1 1 1 1 1 0 1 0 1 0 1 4.4 5.4
## 93 0 1 1 0 1 1 1 0 1 0 0 1 1 0 1 0 0 4.7 5.6
## 94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.0 4.6
## 95 1 1 0 1 0 1 1 1 0 1 1 1 1 0 1 1 1 4.7 4.8
## 96 0 1 1 0 1 0 1 1 0 1 0 0 1 0 0 0 1 4.8 5.1
## 97 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 5.3 5.0
## 98 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.3 6.7
## 99 0 1 1 1 0 1 0 1 0 0 0 0 0 0 0 1 1 4.9 5.2
## 100 0 0 0 0 1 1 0 0 0 1 0 1 1 1 1 1 1 4.9 5.1
## 101 0 0 1 1 1 1 0 0 1 0 0 1 1 0 0 0 1 4.1 4.7
## 102 1 1 1 1 0 0 0 1 0 1 1 1 1 1 0 1 1 5.1 4.8
## 103 0 0 0 0 1 1 0 0 1 0 1 1 1 1 1 1 1 5.5 5.1
## 104 0 1 0 1 1 0 1 1 1 0 1 1 0 1 0 0 0 5.1 4.5
## 105 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 6.2 6.1
## 106 0 0 0 1 1 0 0 0 1 0 1 1 1 1 1 1 1 4.9 5.5
## 107 0 1 0 1 1 0 1 0 1 0 0 0 0 1 1 0 0 4.4 4.9
## 108 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 6.4 4.9
## 109 0 0 0 0 0 1 1 1 1 0 1 1 1 0 0 1 1 5.2 4.8
## 110 0 1 0 1 1 1 1 0 0 0 0 1 1 1 1 1 0 6.2 6.5
## 111 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 6.5 5.5
## 112 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 0 1 4.5 5.5
## 113 1 1 1 0 1 1 1 1 1 0 1 1 0 0 1 1 1 5.1 5.8
## 114 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 6.4 6.3
## 115 1 1 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 5.2 5.3
## 116 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 5.9 6.2
## 117 0 1 1 1 1 1 0 0 0 1 0 1 0 1 0 1 1 5.1 4.4
## 118 1 1 1 0 1 1 1 1 1 1 0 0 1 1 0 1 0 5.5 4.8
## 119 0 1 1 0 1 0 1 0 0 0 0 1 1 1 0 0 0 4.1 4.3
## 120 0 0 1 1 1 0 1 1 1 1 0 0 0 1 0 1 1 4.5 4.4
## 121 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 6.0 6.2
## 122 0 0 1 1 1 0 1 1 0 0 0 1 1 0 0 1 0 4.0 4.5
## 123 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.7 6.5
## 124 0 1 1 1 1 1 1 1 0 1 0 0 0 0 0 1 1 4.5 4.6
## 125 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 5.9 6.1
## 126 1 1 1 0 1 1 1 1 0 1 0 1 1 0 0 1 1 4.9 5.1
## 127 0 1 1 0 0 1 0 1 1 1 1 0 0 0 1 1 1 4.8 4.5
## 128 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 4.4 4.3
## 129 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 5.1 5
## 130 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 1 1 5.1 4.9
## 131 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 6.9 6.8
## 132 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 5.7 6.3
## 133 0 0 1 0 1 0 1 0 0 0 0 1 1 0 0 1 0 4 4
## 134 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.5 4.3
## 135 0 1 0 1 1 1 0 1 1 0 1 0 1 0 0 0 0 4.6 5
## 136 0 0 0 1 0 1 1 1 0 0 1 0 0 0 1 1 0 3.1 4.7
## 137 0 1 0 1 1 1 0 1 1 1 1 0 0 1 0 0 0 4 5.1
## 138 1 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 1 5.8 5.9
## 139 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 4.3 5.7
## 140 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 1 1 3.4 5.6
## 141 0 1 1 1 0 0 1 1 1 0 0 0 1 1 0 0 1 4.4 3
## 142 0 0 1 0 0 1 0 1 1 1 1 1 0 0 0 1 1 4.6 4.7
## 143 0 1 1 1 0 1 1 0 0 0 0 0 1 0 0 1 1 4.3 4.5
## 144 0 0 1 1 1 0 1 1 1 1 0 1 1 0 0 0 1 5 5.1
## 145 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 6 5.9
## 146 1 1 1 1 1 1 0 0 0 1 1 1 1 1 0 1 1 4.1 4.3
## 147 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.1 5.6
## 148 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 5.9 6
## 149 0 0 1 1 1 0 0 0 0 1 0 1 0 0 0 0 0 2.3 4.5
## 150 1 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 0 5 5.2
## 151 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 6.3 6
## 152 0 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 0 4.3 4.2
## 153 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 5.2 5.6
## 154 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 6.1 6.2
## 155 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.1 7
## 156 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5.9 6.3
## 157 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.6 6.7
## 158 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 5 5.1
## 159 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.6 6.8
## 160 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 6.1 5.8
## 161 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 5.4 5.7
## 162 0 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 5 4.1
## 163 1 1 1 1 1 1 1 0 0 0 1 1 1 0 0 1 1 4.4 4.5
## 164 0 1 0 1 1 1 0 0 1 0 1 1 0 0 0 1 1 4.9 5
## 165 1 1 0 1 1 1 1 0 0 1 0 1 1 1 0 1 1 4.9 6.3
## 166 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 6 6.2
## 167 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 5.6 5.4
## 168 0 1 1 0 1 1 1 1 0 0 1 0 0 0 0 1 1 4 4.5
## 169 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.3 6.7
## 170 0 0 0 0 1 1 1 1 1 1 0 1 1 0 0 1 1 4.5 4.7
## 171 0 1 1 1 0 1 1 0 0 1 0 1 1 1 0 0 1 4.1 4
## 172 1 0 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 5 4.6
## 173 1 0 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 5.1 4.3
## 174 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 4.9 4.4
## 175 0 0 0 0 1 1 1 1 1 1 0 1 1 0 0 1 1 4.1 4.1
## 176 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6
## 177 0 0 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1 4 4.1
## 178 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 1 1 5.1 5.8
## 179 1 1 1 0 1 1 0 1 1 1 1 1 1 0 0 1 1 4.4 4
## 180 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.2 6.1
## 181 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 5.9 6.1
## 182 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 5.8
## 183 0 1 1 0 1 1 1 0 0 0 1 1 0 1 0 1 1 5.5 5.6
## 184 0 1 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 2.4 5
## 185 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 5.9 6.6
## P.HIS P.GEN CURSO SEXO EDAD T.LICEO suma.X
## 1 4.4 4.5 4E M 17 4 4
## 2 4.5 4.9 4E M 17 4 4
## 3 4.7 4.5 4E M 18 4 6
## 4 4.1 4.4 4E M 17 4 4
## 5 6.1 6.0 4E F 17 4 7
## 6 6.4 6.4 4E F 19 1 14
## 7 5.8 5.8 4E F 21 5 13
## 8 4.9 5.3 4E M 17 4 10
## 9 5.6 5.7 4E M 17 2 9
## 10 4.4 4.6 4E F 17 1 4
## 11 5.3 5.7 4E M 18 2 13
## 12 4.9 4.8 4E F 17 4 5
## 13 4.7 5.1 4E M 18 2 12
## 14 5.1 5.2 4E M 17 4 9
## 15 4.4 4.9 4E F 18 3 5
## 16 5.2 4.7 4E M 18 2 5
## 17 5.1 4.8 4E F 19 2 6
## 18 5.4 5.8 3D F 16 3 13
## 19 6.5 6.6 3D F 16 3 16
## 20 6.1 6.4 3D F 17 1 16
## 21 4.4 5.4 3D F 17 3 10
## 22 5.0 4.9 3D M 16 2 7
## 23 5.5 6.0 3D F 16 2 12
## 24 5.2 5.0 3D F 16 3 13
## 25 4.8 5.0 3D M 17 4 10
## 26 4.7 4.4 3D F 16 3 2
## 27 6.2 6.3 3D M 17 4 14
## 28 6.3 6.0 3D M 18 1 15
## 29 4.0 4.7 3B M 16 2 7
## 30 4.4 4.5 3B M 16 3 5
## 31 4.3 5.1 3B F 16 3 8
## 32 5.9 5.9 3B F 16 3 13
## 33 5.0 4.8 3B M 16 2 5
## 34 4.4 4.3 3B M 16 3 3
## 35 4.9 5.1 3B M 18 1 12
## 36 5.5 5.0 3B M 16 3 10
## 37 5.1 5.0 3B M 16 3 9
## 38 4.9 4.1 3B F 16 3 6
## 39 4.5 4.7 3B M 16 3 6
## 40 5.6 5.7 3B F 16 3 9
## 41 5.1 5.3 4F F 17 4 11
## 42 4.5 5.0 4F F 17 4 12
## 43 4.6 5.1 4F F 17 2 11
## 44 5.1 5.0 4F F 18 4 7
## 45 6.3 6.4 4F F 18 4 14
## 46 4.8 5.1 4F F 17 4 12
## 47 6.8 6.7 4F F 16 4 16
## 48 5.2 5.4 4F F 16 4 13
## 49 5.1 4.9 4F F 18 2 10
## 50 4.4 5.0 4F M 17 4 10
## 51 6.3 6.4 4F F 19 2 14
## 52 4.6 4.8 4F F 17 4 6
## 53 4.9 5.3 4F F 18 4 10
## 54 5.3 5.7 4F M 20 5 13
## 55 4.9 5.2 4F M 18 2 13
## 56 4.3 5.1 4F F 17 4 12
## 57 4.9 5.2 4F F 17 3 11
## 58 5.5 5.7 4F F 18 1 13
## 59 5.9 5.5 4F F 17 3 10
## 60 5.1 5.0 4F F 17 2 12
## 61 5.0 5.1 1A M 13 1 9
## 62 4.0 4.3 1A M 13 1 2
## 63 5.3 5.5 1A M 15 1 9
## 64 5.5 5.2 1A M 14 1 11
## 65 5.5 5.3 1A M 16 1 9
## 66 5.9 5.6 1A M 14 1 13
## 67 5.1 5.0 1A M 15 1 10
## 68 6.5 6.7 1A F 16 1 16
## 69 4.9 5.4 1A M 14 1 9
## 70 6.2 6.6 1A M 14 1 16
## 71 6.2 6.1 1A M 15 1 14
## 72 6.7 6.5 1A F 14 1 16
## 73 6.7 6.6 1A M 14 1 16
## 74 6.3 6.7 1A M 14 1 16
## 75 6.2 6.3 1A F 15 1 14
## 76 6.1 6.2 1A F 14 1 15
## 77 5.1 5.7 1A F 14 1 13
## 78 4.4 5.0 1A F 13 1 8
## 79 5.4 5.0 1A M 15 1 12
## 80 5.3 5.0 1A M 15 1 8
## 81 6.5 6.6 1A F 14 1 16
## 82 6.3 6.2 3C F 16 NA 14
## 83 4.5 5.1 3C F 16 3 13
## 84 4.4 5.1 3C F 16 3 11
## 85 5.1 4.8 3C F 17 3 6
## 86 5.3 4.9 3C F 16 3 11
## 87 5.4 5.1 3C F 16 3 8
## 88 6.1 6.3 3C F 16 1 14
## 89 5.2 5.1 3C F 16 3 8
## 90 6.2 6.4 3C F 16 3 14
## 91 5.5 5.0 3C F 16 3 12
## 92 4.7 5.2 3C F 16 NA 11
## 93 5.3 5.2 3C F 17 NA 9
## 94 4.5 4.4 3C F 16 2 0
## 95 4.4 4.5 3C F 16 3 12
## 96 4.9 5.0 3C F 16 3 8
## 97 5.6 5.5 3C F 16 2 13
## 98 6.4 6.5 3C F 16 3 15
## 99 5.1 4.9 3C F 16 3 7
## 100 4.9 5.4 1D F 15 1 9
## 101 4.4 4.3 1D F 15 2 8
## 102 4.1 4.7 1D F 14 1 11
## 103 4.3 4.9 1D F 15 1 10
## 104 4.9 5.2 1D M 14 1 9
## 105 6.2 6.3 1D F 14 1 14
## 106 5.7 4.8 1D M 16 2 10
## 107 4.8 4.6 1D M 16 1 7
## 108 5.8 5.9 1D F 14 1 13
## 109 4.9 5.1 1D F 15 1 9
## 110 6.0 6.1 1D M 17 1 10
## 111 5.9 6.2 1D M 13 1 13
## 112 4.3 4.8 1D M 14 1 13
## 113 4.2 4.8 1D F 14 1 12
## 114 6.1 6.3 1D M 13 1 14
## 115 4.4 5.2 1D M 14 1 12
## 116 5.1 5.7 1D F 16 1 13
## 117 5.8 5.0 1D F 14 1 10
## 118 5.6 5.1 1D M 16 1 11
## 119 4.5 4.4 1D F 14 1 7
## 120 5.7 4.9 1D F 14 1 10
## 121 6.3 6.2 1D M 14 1 14
## 122 4.0 4.2 1D F 14 1 8
## 123 6.6 6.1 2E F 17 2 15
## 124 4.5 4.7 2E F 15 2 10
## 125 5.9 5.9 2E F 16 2 14
## 126 4.8 4.7 2E F 15 2 11
## 127 4.8 4.8 2E F 14 2 10
## 128 4.6 4.4 2E F 16 1 4
## 129 5.3 5.2 2E M 15 2 14
## 130 4.3 5.3 2E F 15 2 12
## 131 6.6 6.4 2E F 15 1 15
## 132 6.3 6.1 2E F 16 1 11
## 133 4.3 4.7 2E M 15 2 6
## 134 4.4 4.1 2E M 15 2 0
## 135 4.1 4 2E M 17 3 8
## 136 4.1 4.6 2E F 17 2 7
## 137 4.6 4.8 2E M 15 2 9
## 138 6.1 6 2E F 15 2 13
## 139 6 6.1 2E F 15 2 13
## 140 6 4.7 2E M 15 2 6
## 141 4.2 4.6 2E M 15 2 9
## 142 3 4.8 2E F 15 1 9
## 143 5.3 5.6 2E M 16 2 8
## 144 4.3 5.6 2E F 18 2 10
## 145 6.2 6.4 2B M 17 2 14
## 146 4.8 4.5 2B F 15 2 12
## 147 5.3 5.9 2B M 15 2 15
## 148 5.8 5 2B F 14 2 15
## 149 4.1 4.3 2B M 16 3 5
## 150 4.3 5.2 2B F 16 2 11
## 151 5.8 6.1 2B F 15 1 13
## 152 4.5 4.6 2B F 15 1 10
## 153 5.2 5.4 2B F 15 2 13
## 154 5.8 5.4 2B F 15 1 15
## 155 6.4 6.2 2B M 17 1 16
## 156 6.6 6.5 2B M 15 2 16
## 157 6.4 6.7 2B M 16 2 16
## 158 5.3 5.4 2B F 15 1 13
## 159 6.5 6.7 2B M 16 1 16
## 160 6.2 6.3 2B M 17 1 15
## 161 5.8 5 2B F 15 1 14
## 162 4.3 4.4 2B F 15 1 9
## 163 4.3 5 2B M 15 2 11
## 164 5.1 5 1E F 16 1 9
## 165 4 4.3 1E F 14 1 11
## 166 6.3 6 1E M 15 1 15
## 167 5.7 5.1 1E F 15 1 14
## 168 4.3 4.8 1E F 14 1 9
## 169 6 6.4 1E F 14 1 15
## 170 4.3 49 1E F 13 1 10
## 171 3.3 4.5 1E M 14 1 10
## 172 5.1 4.3 1E F 14 1 11
## 173 4.6 5.2 1E F 16 1 11
## 174 4.7 4.3 1E F 14 1 14
## 175 4 5.4 1E F 14 1 10
## 176 6.1 6.2 1E F 14 1 16
## 177 4.4 4.1 1E F 14 1 8
## 178 5.6 5.1 1E F 15 2 9
## 179 5.1 4.3 1E F 15 1 12
## 180 5.4 5.3 1E M 15 1 16
## 181 6.3 6.4 1E M 14 1 15
## 182 6.2 5.4 1E F 14 1 16
## 183 5.4 5.5 1E F 14 1 10
## 184 5.1 5.3 1E F 14 1 5
## 185 5.1 5.6 1E M 14 1 14ep$EDAD <- as.factor(as.numeric(ep$EDAD))
ep$X11 <- as.factor(as.numeric(ep$X11))
ep$X12 <- as.factor(as.numeric(ep$X12))
ep$X13 <- as.factor(as.numeric(ep$X13))
ep$X14 <- as.factor(as.numeric(ep$X14))
ep$X15 <- as.factor(as.numeric(ep$X15))
ep$X16 <- as.factor(as.numeric(ep$X16))
ep$X1 <- as.factor(as.numeric(ep$X1))
ep$X2 <- as.factor(as.numeric(ep$X2))
ep$X3 <- as.factor(as.numeric(ep$X3))
ep$X4 <- as.factor(as.numeric(ep$X4))
ep$X5 <- as.factor(as.numeric(ep$X5))
ep$X6 <- as.factor(as.numeric(ep$X6))
ep$X7 <- as.factor(as.numeric(ep$X7))
ep$X8 <- as.factor(as.numeric(ep$X8))
ep$X9 <- as.factor(as.numeric(ep$X9))
ep$X10 <- as.factor(as.numeric(ep$X10))
ep$YY <- as.factor(as.numeric(ep$YY))
ep$T.LICEO <- as.factor(as.numeric(ep$T.LICEO))
ep$suma.X <- as.factor(as.numeric(ep$suma.X))# positivo = 1 = (100 = YY)
# negativo = 0 = (85 = YY)
summary(ep)## YY X1 X2 X3 X4 X5 X6 X7 X8
## 0: 85 0: 62 0: 67 0: 56 0: 36 0: 48 0: 56 0: 45 0: 84
## 1:100 1:123 1:118 1:129 1:149 1:137 1:129 1:140 1:101
##
##
##
##
##
## X9 X10 X11 X12 X13 X14 X15 X16
## 0: 60 0: 59 0: 51 0: 61 0:97 0:106 0: 50 0: 33
## 1:125 1:126 1:134 1:124 1:88 1: 79 1:135 1:152
##
##
##
##
##
## P.MAT P.LEN P.HIS P.GEN CURSO
## 4.9 : 12 5.1 : 17 5.1 : 17 5.0 : 15 1D :23
## 5.1 : 12 4.8 : 14 4.4 : 14 5.1 : 15 1E :22
## 5.2 : 12 5.2 : 11 4.3 : 12 4.8 : 12 2E :22
## 5.5 : 12 5.9 : 9 4.9 : 12 5.2 : 11 1A :21
## 4.4 : 9 4.5 : 8 5.3 : 10 5.4 : 9 4F :20
## 5.9 : 9 4.7 : 8 6.2 : 9 6.4 : 9 2B :19
## (Other):119 (Other):118 (Other):111 (Other):114 (Other):58
## SEXO EDAD T.LICEO suma.X
## F:114 16 :51 1 :83 10 :22
## M: 71 15 :39 2 :45 13 :22
## 14 :38 3 :31 9 :19
## 17 :32 4 :21 14 :19
## 18 :14 5 : 2 11 :16
## 13 : 6 NA's: 3 16 :16
## (Other): 5 (Other):71ep## YY X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 P.MAT P.LEN
## 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 4.6 4.8
## 2 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 5.0 5.1
## 3 0 0 1 0 1 1 1 0 0 0 0 1 1 0 0 0 0 4.6 4.5
## 4 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 4.3 4.8
## 5 0 0 0 0 1 1 1 1 0 0 0 1 0 0 0 1 1 6.4 5.8
## 6 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 6.3 6.3
## 7 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 1 5.5 5.9
## 8 0 1 1 1 0 1 0 1 0 0 1 1 1 0 0 1 1 5.0 5.5
## 9 0 0 1 0 1 1 1 1 0 1 1 0 0 0 0 1 1 5.9 5.2
## 10 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 4.4 4.8
## 11 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 5.5 5.9
## 12 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 1 4.5 5.0
## 13 1 1 1 0 1 1 0 1 1 1 1 1 1 0 0 1 1 5.5 4.8
## 14 0 1 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 5.0 5.2
## 15 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 5.1 5.1
## 16 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 4.7 4.3
## 17 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 1 1 4.4 4.8
## 18 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 5.7 5.9
## 19 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.5 6.8
## 20 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.6 6.6
## 21 0 1 1 1 1 1 1 1 0 0 1 0 1 0 0 0 1 5.1 5.5
## 22 0 0 1 1 0 0 0 0 0 1 0 0 1 0 1 1 1 4.2 4.8
## 23 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 6.4 5.9
## 24 1 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 5.4 4.9
## 25 0 1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 1 4.4 5.2
## 26 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 4.1 4.0
## 27 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 6.1 6.2
## 28 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 6.2 6.1
## 29 0 0 0 1 1 1 0 1 0 1 0 1 0 1 0 0 0 4.9 5.1
## 30 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 1 4.2 4.7
## 31 0 0 1 1 0 1 0 1 1 1 1 0 0 0 0 0 1 4.9 5.5
## 32 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 5.4 6.1
## 33 0 1 1 0 0 0 0 1 1 0 0 1 0 0 0 0 0 4.1 4.4
## 34 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 4.1 4.0
## 35 1 1 0 1 1 1 1 1 1 1 1 0 0 1 0 1 1 5.3 5.2
## 36 0 0 1 1 1 1 1 1 0 1 0 1 0 1 0 0 1 5.2 5.1
## 37 0 1 1 0 1 0 1 0 1 0 0 1 1 1 0 1 0 5.3 5.0
## 38 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 0 1 4.0 4.0
## 39 0 0 0 1 1 0 0 1 0 0 1 1 0 0 0 0 1 4.8 4.8
## 40 0 0 0 1 1 0 0 1 1 0 1 1 1 0 0 1 1 5.5 5.4
## 41 1 1 1 0 1 1 0 0 1 1 0 1 0 1 1 1 1 5.2 5.5
## 42 1 1 0 0 1 1 1 1 0 1 1 1 1 1 0 1 1 5.1 5.2
## 43 1 1 1 1 0 1 1 1 0 1 1 0 1 0 0 1 1 5.6 5.1
## 44 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 0 1 4.9 4.7
## 45 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 6.3 6.7
## 46 1 1 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 5.3 4.9
## 47 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.5 6.3
## 48 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 5.5 5.4
## 49 0 1 0 1 1 0 1 0 0 0 1 1 0 1 1 1 1 5.2 4.8
## 50 0 0 0 0 1 1 0 1 1 1 1 1 0 0 1 1 1 5.2 5.3
## 51 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 6.2 6.4
## 52 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0 0 1 4.8 5.1
## 53 0 1 0 1 1 1 1 1 0 0 1 0 0 0 1 1 1 5.5 5.1
## 54 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 1 1 5.9 5.9
## 55 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 5.4 5.5
## 56 1 1 0 1 1 0 1 1 1 1 1 0 1 0 1 1 1 5.2 5.0
## 57 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0 1 1 5.3 5.1
## 58 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1 5.8 5.0
## 59 0 0 1 0 1 0 1 1 1 1 1 1 1 0 0 0 1 5.6 5.2
## 60 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 5.2 4.7
## 61 0 0 1 0 1 1 0 0 0 1 1 1 1 0 1 1 0 4.9 5.0
## 62 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 4.5 4.4
## 63 0 1 1 0 1 0 0 1 1 1 0 1 1 0 0 0 1 5.1 5.7
## 64 1 0 0 1 1 1 0 1 1 1 1 1 0 1 0 1 1 5.4 5.1
## 65 0 1 1 1 1 0 1 0 0 0 1 0 1 0 1 0 1 5.0 5.2
## 66 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 5.9 5.3
## 67 0 1 1 0 1 1 1 1 0 1 1 1 0 0 1 0 0 5.2 5.2
## 68 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.7 6.8
## 69 0 1 0 1 1 1 0 1 0 0 0 1 1 0 0 1 1 5.2 5.3
## 70 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.8 6.7
## 71 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 6.2 6.4
## 72 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.6 6.5
## 73 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.9 6.6
## 74 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.7 6.5
## 75 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 6.1 6.3
## 76 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 6.4 6.2
## 77 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 5.3 5.9
## 78 0 0 1 1 1 1 0 1 0 1 1 0 0 0 0 1 0 5.3 5.2
## 79 1 1 1 0 1 0 1 1 1 0 1 1 1 1 0 1 1 5.4 4.6
## 80 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 4.8 5.1
## 81 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.8 6.7
## 82 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 6.4 6.1
## 83 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 5.5 4.8
## 84 1 1 1 1 1 0 1 1 1 1 0 1 0 0 1 0 1 4.9 5.9
## 85 0 1 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 5.0 4.7
## 86 1 1 0 1 1 1 1 1 0 0 1 1 1 0 0 1 1 5.5 4.3
## 87 0 0 1 1 0 1 1 1 0 0 0 0 1 0 0 1 1 5.0 4.8
## 88 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.0 6.4
## 89 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 1 0 5.2 4.9
## 90 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 6.3 6.2
## 91 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 0 5.5 4.6
## 92 1 1 1 0 0 1 1 1 1 1 1 0 1 0 1 0 1 4.4 5.4
## 93 0 1 1 0 1 1 1 0 1 0 0 1 1 0 1 0 0 4.7 5.6
## 94 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.0 4.6
## 95 1 1 0 1 0 1 1 1 0 1 1 1 1 0 1 1 1 4.7 4.8
## 96 0 1 1 0 1 0 1 1 0 1 0 0 1 0 0 0 1 4.8 5.1
## 97 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 5.3 5.0
## 98 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.3 6.7
## 99 0 1 1 1 0 1 0 1 0 0 0 0 0 0 0 1 1 4.9 5.2
## 100 0 0 0 0 1 1 0 0 0 1 0 1 1 1 1 1 1 4.9 5.1
## 101 0 0 1 1 1 1 0 0 1 0 0 1 1 0 0 0 1 4.1 4.7
## 102 1 1 1 1 0 0 0 1 0 1 1 1 1 1 0 1 1 5.1 4.8
## 103 0 0 0 0 1 1 0 0 1 0 1 1 1 1 1 1 1 5.5 5.1
## 104 0 1 0 1 1 0 1 1 1 0 1 1 0 1 0 0 0 5.1 4.5
## 105 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 6.2 6.1
## 106 0 0 0 1 1 0 0 0 1 0 1 1 1 1 1 1 1 4.9 5.5
## 107 0 1 0 1 1 0 1 0 1 0 0 0 0 1 1 0 0 4.4 4.9
## 108 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 6.4 4.9
## 109 0 0 0 0 0 1 1 1 1 0 1 1 1 0 0 1 1 5.2 4.8
## 110 0 1 0 1 1 1 1 0 0 0 0 1 1 1 1 1 0 6.2 6.5
## 111 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 6.5 5.5
## 112 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 0 1 4.5 5.5
## 113 1 1 1 0 1 1 1 1 1 0 1 1 0 0 1 1 1 5.1 5.8
## 114 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 6.4 6.3
## 115 1 1 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 5.2 5.3
## 116 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 5.9 6.2
## 117 0 1 1 1 1 1 0 0 0 1 0 1 0 1 0 1 1 5.1 4.4
## 118 1 1 1 0 1 1 1 1 1 1 0 0 1 1 0 1 0 5.5 4.8
## 119 0 1 1 0 1 0 1 0 0 0 0 1 1 1 0 0 0 4.1 4.3
## 120 0 0 1 1 1 0 1 1 1 1 0 0 0 1 0 1 1 4.5 4.4
## 121 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 6.0 6.2
## 122 0 0 1 1 1 0 1 1 0 0 0 1 1 0 0 1 0 4.0 4.5
## 123 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.7 6.5
## 124 0 1 1 1 1 1 1 1 0 1 0 0 0 0 0 1 1 4.5 4.6
## 125 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 5.9 6.1
## 126 1 1 1 0 1 1 1 1 0 1 0 1 1 0 0 1 1 4.9 5.1
## 127 0 1 1 0 0 1 0 1 1 1 1 0 0 0 1 1 1 4.8 4.5
## 128 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 4.4 4.3
## 129 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 5.1 5
## 130 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 1 1 5.1 4.9
## 131 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 6.9 6.8
## 132 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 5.7 6.3
## 133 0 0 1 0 1 0 1 0 0 0 0 1 1 0 0 1 0 4 4
## 134 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.5 4.3
## 135 0 1 0 1 1 1 0 1 1 0 1 0 1 0 0 0 0 4.6 5
## 136 0 0 0 1 0 1 1 1 0 0 1 0 0 0 1 1 0 3.1 4.7
## 137 0 1 0 1 1 1 0 1 1 1 1 0 0 1 0 0 0 4 5.1
## 138 1 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 1 5.8 5.9
## 139 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 4.3 5.7
## 140 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 1 1 3.4 5.6
## 141 0 1 1 1 0 0 1 1 1 0 0 0 1 1 0 0 1 4.4 3
## 142 0 0 1 0 0 1 0 1 1 1 1 1 0 0 0 1 1 4.6 4.7
## 143 0 1 1 1 0 1 1 0 0 0 0 0 1 0 0 1 1 4.3 4.5
## 144 0 0 1 1 1 0 1 1 1 1 0 1 1 0 0 0 1 5 5.1
## 145 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 6 5.9
## 146 1 1 1 1 1 1 0 0 0 1 1 1 1 1 0 1 1 4.1 4.3
## 147 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.1 5.6
## 148 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 5.9 6
## 149 0 0 1 1 1 0 0 0 0 1 0 1 0 0 0 0 0 2.3 4.5
## 150 1 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 0 5 5.2
## 151 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 6.3 6
## 152 0 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 0 4.3 4.2
## 153 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 5.2 5.6
## 154 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 6.1 6.2
## 155 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.1 7
## 156 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5.9 6.3
## 157 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.6 6.7
## 158 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 5 5.1
## 159 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.6 6.8
## 160 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 6.1 5.8
## 161 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 5.4 5.7
## 162 0 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 5 4.1
## 163 1 1 1 1 1 1 1 0 0 0 1 1 1 0 0 1 1 4.4 4.5
## 164 0 1 0 1 1 1 0 0 1 0 1 1 0 0 0 1 1 4.9 5
## 165 1 1 0 1 1 1 1 0 0 1 0 1 1 1 0 1 1 4.9 6.3
## 166 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 6 6.2
## 167 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 5.6 5.4
## 168 0 1 1 0 1 1 1 1 0 0 1 0 0 0 0 1 1 4 4.5
## 169 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.3 6.7
## 170 0 0 0 0 1 1 1 1 1 1 0 1 1 0 0 1 1 4.5 4.7
## 171 0 1 1 1 0 1 1 0 0 1 0 1 1 1 0 0 1 4.1 4
## 172 1 0 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 5 4.6
## 173 1 0 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 5.1 4.3
## 174 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 4.9 4.4
## 175 0 0 0 0 1 1 1 1 1 1 0 1 1 0 0 1 1 4.1 4.1
## 176 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6
## 177 0 0 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1 4 4.1
## 178 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 1 1 5.1 5.8
## 179 1 1 1 0 1 1 0 1 1 1 1 1 1 0 0 1 1 4.4 4
## 180 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6.2 6.1
## 181 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 5.9 6.1
## 182 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 5.8
## 183 0 1 1 0 1 1 1 0 0 0 1 1 0 1 0 1 1 5.5 5.6
## 184 0 1 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 2.4 5
## 185 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 5.9 6.6
## P.HIS P.GEN CURSO SEXO EDAD T.LICEO suma.X
## 1 4.4 4.5 4E M 17 4 4
## 2 4.5 4.9 4E M 17 4 4
## 3 4.7 4.5 4E M 18 4 6
## 4 4.1 4.4 4E M 17 4 4
## 5 6.1 6.0 4E F 17 4 7
## 6 6.4 6.4 4E F 19 1 14
## 7 5.8 5.8 4E F 21 5 13
## 8 4.9 5.3 4E M 17 4 10
## 9 5.6 5.7 4E M 17 2 9
## 10 4.4 4.6 4E F 17 1 4
## 11 5.3 5.7 4E M 18 2 13
## 12 4.9 4.8 4E F 17 4 5
## 13 4.7 5.1 4E M 18 2 12
## 14 5.1 5.2 4E M 17 4 9
## 15 4.4 4.9 4E F 18 3 5
## 16 5.2 4.7 4E M 18 2 5
## 17 5.1 4.8 4E F 19 2 6
## 18 5.4 5.8 3D F 16 3 13
## 19 6.5 6.6 3D F 16 3 16
## 20 6.1 6.4 3D F 17 1 16
## 21 4.4 5.4 3D F 17 3 10
## 22 5.0 4.9 3D M 16 2 7
## 23 5.5 6.0 3D F 16 2 12
## 24 5.2 5.0 3D F 16 3 13
## 25 4.8 5.0 3D M 17 4 10
## 26 4.7 4.4 3D F 16 3 2
## 27 6.2 6.3 3D M 17 4 14
## 28 6.3 6.0 3D M 18 1 15
## 29 4.0 4.7 3B M 16 2 7
## 30 4.4 4.5 3B M 16 3 5
## 31 4.3 5.1 3B F 16 3 8
## 32 5.9 5.9 3B F 16 3 13
## 33 5.0 4.8 3B M 16 2 5
## 34 4.4 4.3 3B M 16 3 3
## 35 4.9 5.1 3B M 18 1 12
## 36 5.5 5.0 3B M 16 3 10
## 37 5.1 5.0 3B M 16 3 9
## 38 4.9 4.1 3B F 16 3 6
## 39 4.5 4.7 3B M 16 3 6
## 40 5.6 5.7 3B F 16 3 9
## 41 5.1 5.3 4F F 17 4 11
## 42 4.5 5.0 4F F 17 4 12
## 43 4.6 5.1 4F F 17 2 11
## 44 5.1 5.0 4F F 18 4 7
## 45 6.3 6.4 4F F 18 4 14
## 46 4.8 5.1 4F F 17 4 12
## 47 6.8 6.7 4F F 16 4 16
## 48 5.2 5.4 4F F 16 4 13
## 49 5.1 4.9 4F F 18 2 10
## 50 4.4 5.0 4F M 17 4 10
## 51 6.3 6.4 4F F 19 2 14
## 52 4.6 4.8 4F F 17 4 6
## 53 4.9 5.3 4F F 18 4 10
## 54 5.3 5.7 4F M 20 5 13
## 55 4.9 5.2 4F M 18 2 13
## 56 4.3 5.1 4F F 17 4 12
## 57 4.9 5.2 4F F 17 3 11
## 58 5.5 5.7 4F F 18 1 13
## 59 5.9 5.5 4F F 17 3 10
## 60 5.1 5.0 4F F 17 2 12
## 61 5.0 5.1 1A M 13 1 9
## 62 4.0 4.3 1A M 13 1 2
## 63 5.3 5.5 1A M 15 1 9
## 64 5.5 5.2 1A M 14 1 11
## 65 5.5 5.3 1A M 16 1 9
## 66 5.9 5.6 1A M 14 1 13
## 67 5.1 5.0 1A M 15 1 10
## 68 6.5 6.7 1A F 16 1 16
## 69 4.9 5.4 1A M 14 1 9
## 70 6.2 6.6 1A M 14 1 16
## 71 6.2 6.1 1A M 15 1 14
## 72 6.7 6.5 1A F 14 1 16
## 73 6.7 6.6 1A M 14 1 16
## 74 6.3 6.7 1A M 14 1 16
## 75 6.2 6.3 1A F 15 1 14
## 76 6.1 6.2 1A F 14 1 15
## 77 5.1 5.7 1A F 14 1 13
## 78 4.4 5.0 1A F 13 1 8
## 79 5.4 5.0 1A M 15 1 12
## 80 5.3 5.0 1A M 15 1 8
## 81 6.5 6.6 1A F 14 1 16
## 82 6.3 6.2 3C F 16 <NA> 14
## 83 4.5 5.1 3C F 16 3 13
## 84 4.4 5.1 3C F 16 3 11
## 85 5.1 4.8 3C F 17 3 6
## 86 5.3 4.9 3C F 16 3 11
## 87 5.4 5.1 3C F 16 3 8
## 88 6.1 6.3 3C F 16 1 14
## 89 5.2 5.1 3C F 16 3 8
## 90 6.2 6.4 3C F 16 3 14
## 91 5.5 5.0 3C F 16 3 12
## 92 4.7 5.2 3C F 16 <NA> 11
## 93 5.3 5.2 3C F 17 <NA> 9
## 94 4.5 4.4 3C F 16 2 0
## 95 4.4 4.5 3C F 16 3 12
## 96 4.9 5.0 3C F 16 3 8
## 97 5.6 5.5 3C F 16 2 13
## 98 6.4 6.5 3C F 16 3 15
## 99 5.1 4.9 3C F 16 3 7
## 100 4.9 5.4 1D F 15 1 9
## 101 4.4 4.3 1D F 15 2 8
## 102 4.1 4.7 1D F 14 1 11
## 103 4.3 4.9 1D F 15 1 10
## 104 4.9 5.2 1D M 14 1 9
## 105 6.2 6.3 1D F 14 1 14
## 106 5.7 4.8 1D M 16 2 10
## 107 4.8 4.6 1D M 16 1 7
## 108 5.8 5.9 1D F 14 1 13
## 109 4.9 5.1 1D F 15 1 9
## 110 6.0 6.1 1D M 17 1 10
## 111 5.9 6.2 1D M 13 1 13
## 112 4.3 4.8 1D M 14 1 13
## 113 4.2 4.8 1D F 14 1 12
## 114 6.1 6.3 1D M 13 1 14
## 115 4.4 5.2 1D M 14 1 12
## 116 5.1 5.7 1D F 16 1 13
## 117 5.8 5.0 1D F 14 1 10
## 118 5.6 5.1 1D M 16 1 11
## 119 4.5 4.4 1D F 14 1 7
## 120 5.7 4.9 1D F 14 1 10
## 121 6.3 6.2 1D M 14 1 14
## 122 4.0 4.2 1D F 14 1 8
## 123 6.6 6.1 2E F 17 2 15
## 124 4.5 4.7 2E F 15 2 10
## 125 5.9 5.9 2E F 16 2 14
## 126 4.8 4.7 2E F 15 2 11
## 127 4.8 4.8 2E F 14 2 10
## 128 4.6 4.4 2E F 16 1 4
## 129 5.3 5.2 2E M 15 2 14
## 130 4.3 5.3 2E F 15 2 12
## 131 6.6 6.4 2E F 15 1 15
## 132 6.3 6.1 2E F 16 1 11
## 133 4.3 4.7 2E M 15 2 6
## 134 4.4 4.1 2E M 15 2 0
## 135 4.1 4 2E M 17 3 8
## 136 4.1 4.6 2E F 17 2 7
## 137 4.6 4.8 2E M 15 2 9
## 138 6.1 6 2E F 15 2 13
## 139 6 6.1 2E F 15 2 13
## 140 6 4.7 2E M 15 2 6
## 141 4.2 4.6 2E M 15 2 9
## 142 3 4.8 2E F 15 1 9
## 143 5.3 5.6 2E M 16 2 8
## 144 4.3 5.6 2E F 18 2 10
## 145 6.2 6.4 2B M 17 2 14
## 146 4.8 4.5 2B F 15 2 12
## 147 5.3 5.9 2B M 15 2 15
## 148 5.8 5 2B F 14 2 15
## 149 4.1 4.3 2B M 16 3 5
## 150 4.3 5.2 2B F 16 2 11
## 151 5.8 6.1 2B F 15 1 13
## 152 4.5 4.6 2B F 15 1 10
## 153 5.2 5.4 2B F 15 2 13
## 154 5.8 5.4 2B F 15 1 15
## 155 6.4 6.2 2B M 17 1 16
## 156 6.6 6.5 2B M 15 2 16
## 157 6.4 6.7 2B M 16 2 16
## 158 5.3 5.4 2B F 15 1 13
## 159 6.5 6.7 2B M 16 1 16
## 160 6.2 6.3 2B M 17 1 15
## 161 5.8 5 2B F 15 1 14
## 162 4.3 4.4 2B F 15 1 9
## 163 4.3 5 2B M 15 2 11
## 164 5.1 5 1E F 16 1 9
## 165 4 4.3 1E F 14 1 11
## 166 6.3 6 1E M 15 1 15
## 167 5.7 5.1 1E F 15 1 14
## 168 4.3 4.8 1E F 14 1 9
## 169 6 6.4 1E F 14 1 15
## 170 4.3 49 1E F 13 1 10
## 171 3.3 4.5 1E M 14 1 10
## 172 5.1 4.3 1E F 14 1 11
## 173 4.6 5.2 1E F 16 1 11
## 174 4.7 4.3 1E F 14 1 14
## 175 4 5.4 1E F 14 1 10
## 176 6.1 6.2 1E F 14 1 16
## 177 4.4 4.1 1E F 14 1 8
## 178 5.6 5.1 1E F 15 2 9
## 179 5.1 4.3 1E F 15 1 12
## 180 5.4 5.3 1E M 15 1 16
## 181 6.3 6.4 1E M 14 1 15
## 182 6.2 5.4 1E F 14 1 16
## 183 5.4 5.5 1E F 14 1 10
## 184 5.1 5.3 1E F 14 1 5
## 185 5.1 5.6 1E M 14 1 14set.seed(2019) #semillat.ids <- createDataPartition(ep$YY, p=0.6875, list=F)
mod <- naiveBayes(YY ~ ., data = ep[t.ids,])
mod##
## Naive Bayes Classifier for Discrete Predictors
##
## Call:
## naiveBayes.default(x = X, y = Y, laplace = laplace)
##
## A-priori probabilities:
## Y
## 0 1
## 0.4609375 0.5390625
##
## Conditional probabilities:
## X1
## Y 0 1
## 0 0.5593220 0.4406780
## 1 0.1304348 0.8695652
##
## X2
## Y 0 1
## 0 0.4237288 0.5762712
## 1 0.2608696 0.7391304
##
## X3
## Y 0 1
## 0 0.3898305 0.6101695
## 1 0.2028986 0.7971014
##
## X4
## Y 0 1
## 0 0.32203390 0.67796610
## 1 0.05797101 0.94202899
##
## X5
## Y 0 1
## 0 0.4406780 0.5593220
## 1 0.1304348 0.8695652
##
## X6
## Y 0 1
## 0 0.4406780 0.5593220
## 1 0.1594203 0.8405797
##
## X7
## Y 0 1
## 0 0.47457627 0.52542373
## 1 0.08695652 0.91304348
##
## X8
## Y 0 1
## 0 0.8135593 0.1864407
## 1 0.2318841 0.7681159
##
## X9
## Y 0 1
## 0 0.64406780 0.35593220
## 1 0.08695652 0.91304348
##
## X10
## Y 0 1
## 0 0.72881356 0.27118644
## 1 0.07246377 0.92753623
##
## X11
## Y 0 1
## 0 0.4915254 0.5084746
## 1 0.1159420 0.8840580
##
## X12
## Y 0 1
## 0 0.5593220 0.4406780
## 1 0.1884058 0.8115942
##
## X13
## Y 0 1
## 0 0.7966102 0.2033898
## 1 0.3478261 0.6521739
##
## X14
## Y 0 1
## 0 0.7796610 0.2203390
## 1 0.4347826 0.5652174
##
## X15
## Y 0 1
## 0 0.50847458 0.49152542
## 1 0.08695652 0.91304348
##
## X16
## Y 0 1
## 0 0.30508475 0.69491525
## 1 0.04347826 0.95652174
##
## P.MAT
## Y 2.3 2.4 3.1 3.4 4 4.0
## 0 0.01694915 0.01694915 0.01694915 0.01694915 0.03389831 0.05084746
## 1 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## P.MAT
## Y 4.1 4.2 4.3 4.4 4.5 4.6
## 0 0.11864407 0.03389831 0.03389831 0.06779661 0.06779661 0.03389831
## 1 0.01449275 0.00000000 0.01449275 0.01449275 0.00000000 0.00000000
## P.MAT
## Y 4.7 4.8 4.9 5 5.0 5.1
## 0 0.03389831 0.05084746 0.06779661 0.03389831 0.08474576 0.06779661
## 1 0.00000000 0.00000000 0.04347826 0.04347826 0.00000000 0.05797101
## P.MAT
## Y 5.2 5.3 5.4 5.5 5.6 5.7
## 0 0.06779661 0.01694915 0.00000000 0.01694915 0.00000000 0.00000000
## 1 0.05797101 0.05797101 0.05797101 0.11594203 0.00000000 0.01449275
## P.MAT
## Y 5.8 5.9 6 6.0 6.1 6.2
## 0 0.00000000 0.01694915 0.00000000 0.00000000 0.00000000 0.01694915
## 1 0.01449275 0.07246377 0.02898551 0.02898551 0.07246377 0.05797101
## P.MAT
## Y 6.3 6.4 6.5 6.6 6.7 6.8
## 0 0.00000000 0.01694915 0.00000000 0.00000000 0.00000000 0.00000000
## 1 0.05797101 0.04347826 0.02898551 0.04347826 0.01449275 0.01449275
## P.MAT
## Y 6.9
## 0 0.00000000
## 1 0.02898551
##
## P.LEN
## Y 3 4 4.0 4.1 4.2 4.3
## 0 0.00000000 0.03389831 0.05084746 0.05084746 0.01694915 0.06779661
## 1 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.04347826
## P.LEN
## Y 4.4 4.5 4.6 4.7 4.8 4.9
## 0 0.05084746 0.05084746 0.03389831 0.10169492 0.10169492 0.00000000
## 1 0.01449275 0.01449275 0.04347826 0.00000000 0.04347826 0.05797101
## P.LEN
## Y 5 5.0 5.1 5.2 5.3 5.4
## 0 0.03389831 0.01694915 0.11864407 0.11864407 0.01694915 0.00000000
## 1 0.01449275 0.04347826 0.04347826 0.02898551 0.02898551 0.01449275
## P.LEN
## Y 5.5 5.6 5.7 5.8 5.9 6
## 0 0.03389831 0.03389831 0.01694915 0.03389831 0.00000000 0.00000000
## 1 0.04347826 0.02898551 0.02898551 0.02898551 0.07246377 0.01449275
## P.LEN
## Y 6.1 6.2 6.3 6.4 6.5 6.6
## 0 0.00000000 0.00000000 0.00000000 0.00000000 0.01694915 0.00000000
## 1 0.07246377 0.07246377 0.07246377 0.04347826 0.01449275 0.02898551
## P.LEN
## Y 6.7 6.8 7
## 0 0.00000000 0.00000000 0.00000000
## 1 0.05797101 0.02898551 0.00000000
##
## P.HIS
## Y 3 3.3 4 4.0 4.1 4.2
## 0 0.01694915 0.01694915 0.01694915 0.01694915 0.03389831 0.00000000
## 1 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.01449275
## P.HIS
## Y 4.3 4.4 4.5 4.6 4.7 4.8
## 0 0.05084746 0.15254237 0.10169492 0.03389831 0.01694915 0.01694915
## 1 0.05797101 0.02898551 0.01449275 0.01449275 0.02898551 0.04347826
## P.HIS
## Y 4.9 5.0 5.1 5.2 5.3 5.4
## 0 0.11864407 0.03389831 0.15254237 0.01694915 0.05084746 0.00000000
## 1 0.04347826 0.00000000 0.02898551 0.04347826 0.08695652 0.02898551
## P.HIS
## Y 5.5 5.6 5.7 5.8 5.9 6
## 0 0.03389831 0.03389831 0.01694915 0.01694915 0.00000000 0.01694915
## 1 0.02898551 0.02898551 0.00000000 0.07246377 0.04347826 0.02898551
## P.HIS
## Y 6.0 6.1 6.2 6.3 6.4 6.5
## 0 0.01694915 0.01694915 0.00000000 0.00000000 0.00000000 0.00000000
## 1 0.00000000 0.04347826 0.08695652 0.11594203 0.01449275 0.02898551
## P.HIS
## Y 6.6 6.7 6.8
## 0 0.00000000 0.00000000 0.00000000
## 1 0.04347826 0.01449275 0.01449275
##
## P.GEN
## Y 4 4.1 4.2 4.3 4.4 4.5
## 0 0.00000000 0.05084746 0.01694915 0.05084746 0.08474576 0.05084746
## 1 0.00000000 0.00000000 0.00000000 0.02898551 0.00000000 0.01449275
## P.GEN
## Y 4.6 4.7 4.8 4.9 49 5
## 0 0.05084746 0.08474576 0.10169492 0.08474576 0.00000000 0.01694915
## 1 0.00000000 0.01449275 0.01449275 0.01449275 0.00000000 0.02898551
## P.GEN
## Y 5.0 5.1 5.2 5.3 5.4 5.5
## 0 0.11864407 0.01694915 0.03389831 0.06779661 0.06779661 0.01694915
## 1 0.04347826 0.10144928 0.08695652 0.04347826 0.05797101 0.01449275
## P.GEN
## Y 5.6 5.7 5.8 5.9 6 6.0
## 0 0.03389831 0.01694915 0.00000000 0.00000000 0.00000000 0.01694915
## 1 0.01449275 0.04347826 0.01449275 0.04347826 0.01449275 0.01449275
## P.GEN
## Y 6.1 6.2 6.3 6.4 6.5 6.6
## 0 0.01694915 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 1 0.07246377 0.04347826 0.07246377 0.11594203 0.01449275 0.02898551
## P.GEN
## Y 6.7
## 0 0.00000000
## 1 0.04347826
##
## CURSO
## Y 1A 1D 1E 2B 2E 3B
## 0 0.08474576 0.11864407 0.10169492 0.05084746 0.15254237 0.10169492
## 1 0.08695652 0.10144928 0.10144928 0.20289855 0.11594203 0.01449275
## CURSO
## Y 3C 3D 4E 4F
## 0 0.08474576 0.06779661 0.16949153 0.06779661
## 1 0.11594203 0.05797101 0.04347826 0.15942029
##
## SEXO
## Y F M
## 0 0.5932203 0.4067797
## 1 0.6086957 0.3913043
##
## EDAD
## Y 13 14 15 16 17 18
## 0 0.01694915 0.15254237 0.20338983 0.27118644 0.25423729 0.08474576
## 1 0.02898551 0.15942029 0.27536232 0.26086957 0.13043478 0.10144928
## EDAD
## Y 19 20 21
## 0 0.01694915 0.00000000 0.00000000
## 1 0.01449275 0.01449275 0.01449275
##
## T.LICEO
## Y 1 2 3 4 5
## 0 0.36206897 0.27586207 0.18965517 0.17241379 0.00000000
## 1 0.48529412 0.27941176 0.10294118 0.10294118 0.02941176
##
## suma.X
## Y 0 2 3 4 5 6
## 0 0.03389831 0.01694915 0.01694915 0.06779661 0.10169492 0.11864407
## 1 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## suma.X
## Y 7 8 9 10 11 12
## 0 0.10169492 0.10169492 0.18644068 0.25423729 0.00000000 0.00000000
## 1 0.00000000 0.00000000 0.00000000 0.00000000 0.15942029 0.14492754
## suma.X
## Y 13 14 15 16
## 0 0.00000000 0.00000000 0.00000000 0.00000000
## 1 0.23188406 0.21739130 0.13043478 0.11594203pred <- predict(mod, ep[-t.ids,])
tab <- table(ep[-t.ids,]$YY,pred, dnn=c("Actual", "Predicha"))
confusionMatrix(tab)## Confusion Matrix and Statistics
##
## Predicha
## Actual 0 1
## 0 26 0
## 1 1 30
##
## Accuracy : 0.9825
## 95% CI : (0.9061, 0.9996)
## No Information Rate : 0.5263
## P-Value [Acc > NIR] : 6.754e-15
##
## Kappa : 0.9647
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.9630
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.9677
## Prevalence : 0.4737
## Detection Rate : 0.4561
## Detection Prevalence : 0.4561
## Balanced Accuracy : 0.9815
##
## 'Positive' Class : 0
##