Técnicas de Análisis y Explotación de Datos
Asignatura 3, Foro 2
Alberto Sandoval
Fecha de entrega: 21 de Diciembre 2023
1.Planteamiento
A través de un conjunto de datos (TABLON_ENTRENAMIENTO.csv) entrenar un modelo que realice la predicción de la variable TARGET teniendo en cuenta los inputs dados por las variables (VAR1, VAR2, VAR3, VAR4).
Se debe realizar el test del modelo utilizando los datos del fichero TABLON_VALIDACION.csv. Para ello, elija la métrica que mejor se adapte a la naturaleza de los datos. Pista: la variable TARGET es cuantitativa continua.
Este reto se debe resolver en RStudio y presentar los resultados a través de un documento (html o pdf) creado con RMarkdown.
Debe indagar sobre diferentes naturalezas de modelos predictivos y mostrar cuál de ellos es el que mejor resultado arroja para el caso bajo estudio tras realizar la comparación entre las métricas obtenidas.
2.Resolución
2.1 Cargando Librerias
library(DataExplorer)## Warning: package 'DataExplorer' was built under R version 4.3.2library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(randomForest)## Warning: package 'randomForest' was built under R version 4.3.2## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:dplyr':
##
## combinelibrary(ggplot2)##
## Attaching package: 'ggplot2'## The following object is masked from 'package:randomForest':
##
## marginlibrary(scatterplot3d)2.2 Importando Bases de Datos
test <- read.csv("TABLON_VALIDACION.csv", header = TRUE, sep = ";")
train <- read.csv("TABLON_ENTRENAMIENTO.csv", header = TRUE, sep = ";")Comprobando
#revisando
test## TARGET VAR1 VAR2 VAR3 VAR4
## 1 0.153415 1 0.8 1 300
## 2 0.164236 2 0.8 1 300
## 3 0.182893 3 0.8 1 300
## 4 0.290766 4 0.8 1 300
## 5 0.402376 5 0.8 1 300
## 6 0.544483 6 0.8 1 300
## 7 0.691457 7 0.8 1 300
## 8 0.960261 8 0.8 1 300
## 9 1.226364 9 0.8 1 300
## 10 1.488420 10 0.8 1 300
## 11 1.741314 11 0.8 1 300
## 12 1.952005 12 0.8 1 300
## 13 2.176109 13 0.8 1 300
## 14 2.406063 14 0.8 1 300
## 15 2.496683 15 0.8 1 300
## 16 2.637321 16 0.8 1 300
## 17 2.801653 17 0.8 1 300
## 18 2.907267 18 0.8 1 300
## 19 3.047064 19 0.8 1 300
## 20 3.180145 20 0.8 1 300
## 21 3.262054 21 0.8 1 300
## 22 3.323903 22 0.8 1 300
## 23 3.395564 23 0.8 1 300
## 24 3.486532 24 0.8 1 300
## 25 3.530944 25 0.8 1 300
## 26 3.596784 26 0.8 1 300
## 27 3.665159 27 0.8 1 300
## 28 3.733804 28 0.8 1 300
## 29 3.793173 29 0.8 1 300
## 30 3.867632 30 0.8 1 300
## 31 3.874558 31 0.8 1 300
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## 58 1.159747 8 2.0 1 300
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## 2106 0.691914 6 4.0 6 500
## 2107 0.780493 7 4.0 6 500
## 2108 1.078159 8 4.0 6 500
## 2109 1.391446 9 4.0 6 500
## 2110 1.585421 10 4.0 6 500
## 2111 1.960887 11 4.0 6 500
## 2112 2.076494 12 4.0 6 500
## 2113 2.239849 13 4.0 6 500
## 2114 2.539159 14 4.0 6 500
## 2115 2.645719 15 4.0 6 500
## 2116 2.747935 16 4.0 6 500
## 2117 2.866160 17 4.0 6 500
## 2118 3.074145 18 4.0 6 500
## 2119 3.175218 19 4.0 6 500
## 2120 3.243944 20 4.0 6 500
## 2121 3.409506 21 4.0 6 500
## 2122 3.441093 22 4.0 6 500
## 2123 3.638602 23 4.0 6 500
## 2124 3.561019 24 4.0 6 500
## 2125 3.685686 25 4.0 6 500
## 2126 3.731738 26 4.0 6 500
## 2127 3.851754 27 4.0 6 500
## 2128 3.955193 28 4.0 6 500
## 2129 3.914164 29 4.0 6 500
## 2130 4.069547 30 4.0 6 500
## 2131 4.028659 31 4.0 6 500
## 2132 4.073403 32 4.0 6 500
## 2133 4.172855 33 4.0 6 500
## 2134 4.218201 34 4.0 6 500
## 2135 4.250303 35 4.0 6 500
## 2136 4.272208 36 4.0 6 500
## 2137 4.249387 37 4.0 6 500
## 2138 4.289324 38 4.0 6 500
## 2139 4.373687 39 4.0 6 500
## 2140 4.391722 40 4.0 6 500
## 2141 4.328810 41 4.0 6 500
## 2142 4.346330 42 4.0 6 500
## 2143 4.415204 43 4.0 6 500
## 2144 4.347346 44 4.0 6 500
## 2145 4.393454 45 4.0 6 500
## 2146 4.394723 46 4.0 6 500
## 2147 4.419839 47 4.0 6 500
## 2148 4.437376 48 4.0 6 500
## 2149 4.423460 49 4.0 6 500
## 2150 4.456827 50 4.0 6 500
## 2151 0.307604 1 6.0 6 500
## 2152 0.334389 2 6.0 6 500
## 2153 0.333938 3 6.0 6 500
## 2154 0.424815 4 6.0 6 500
## 2155 0.492987 5 6.0 6 500
## 2156 0.678876 6 6.0 6 500
## 2157 0.857073 7 6.0 6 500
## 2158 1.113284 8 6.0 6 500
## 2159 1.448183 9 6.0 6 500
## 2160 1.529677 10 6.0 6 500
## 2161 1.866246 11 6.0 6 500
## 2162 2.068911 12 6.0 6 500
## 2163 2.407681 13 6.0 6 500
## 2164 2.517518 14 6.0 6 500
## 2165 2.753833 15 6.0 6 500
## 2166 2.876257 16 6.0 6 500
## 2167 2.934521 17 6.0 6 500
## 2168 3.026325 18 6.0 6 500
## 2169 3.352073 19 6.0 6 500
## 2170 3.372945 20 6.0 6 500
## 2171 3.514003 21 6.0 6 500
## 2172 3.562573 22 6.0 6 500
## 2173 3.646494 23 6.0 6 500
## 2174 3.612103 24 6.0 6 500
## 2175 3.734214 25 6.0 6 500
## 2176 3.825962 26 6.0 6 500
## 2177 3.989160 27 6.0 6 500
## 2178 3.942623 28 6.0 6 500
## 2179 4.088950 29 6.0 6 500
## 2180 4.160504 30 6.0 6 500
## 2181 4.163031 31 6.0 6 500
## 2182 4.192223 32 6.0 6 500
## 2183 4.221769 33 6.0 6 500
## 2184 4.345452 34 6.0 6 500
## 2185 4.313646 35 6.0 6 500
## 2186 4.295976 36 6.0 6 500
## 2187 4.333159 37 6.0 6 500
## 2188 4.435492 38 6.0 6 500
## 2189 4.365036 39 6.0 6 500
## 2190 4.404512 40 6.0 6 500
## 2191 4.448730 41 6.0 6 500
## 2192 4.436076 42 6.0 6 500
## 2193 4.423867 43 6.0 6 500
## 2194 4.457303 44 6.0 6 500
## 2195 4.452790 45 6.0 6 500
## 2196 4.463389 46 6.0 6 500
## 2197 4.481076 47 6.0 6 500
## 2198 4.485364 48 6.0 6 500
## 2199 4.499828 49 6.0 6 500
## 2200 4.527340 50 6.0 6 500
## 2201 0.301306 1 8.0 6 500
## 2202 0.303018 2 8.0 6 500
## 2203 0.349234 3 8.0 6 500
## 2204 0.409413 4 8.0 6 500
## 2205 0.558802 5 8.0 6 500
## 2206 0.627067 6 8.0 6 500
## 2207 0.767559 7 8.0 6 500
## 2208 1.182188 8 8.0 6 500
## 2209 1.359991 9 8.0 6 500
## 2210 1.519429 10 8.0 6 500
## 2211 1.976958 11 8.0 6 500
## 2212 2.104916 12 8.0 6 500
## 2213 2.384516 13 8.0 6 500
## 2214 2.618306 14 8.0 6 500
## 2215 2.649296 15 8.0 6 500
## 2216 2.868897 16 8.0 6 500
## 2217 2.934113 17 8.0 6 500
## 2218 3.175496 18 8.0 6 500
## 2219 3.252301 19 8.0 6 500
## 2220 3.380278 20 8.0 6 500
## 2221 3.452696 21 8.0 6 500
## 2222 3.694263 22 8.0 6 500
## 2223 3.668038 23 8.0 6 500
## 2224 3.758589 24 8.0 6 500
## 2225 3.831385 25 8.0 6 500
## 2226 3.871419 26 8.0 6 500
## 2227 4.038390 27 8.0 6 500
## 2228 4.009385 28 8.0 6 500
## 2229 4.158630 29 8.0 6 500
## 2230 4.103868 30 8.0 6 500
## 2231 4.282955 31 8.0 6 500
## 2232 4.227340 32 8.0 6 500
## 2233 4.296433 33 8.0 6 500
## 2234 4.269557 34 8.0 6 500
## 2235 4.392168 35 8.0 6 500
## 2236 4.281332 36 8.0 6 500
## 2237 4.453560 37 8.0 6 500
## 2238 4.450255 38 8.0 6 500
## 2239 4.415992 39 8.0 6 500
## 2240 4.411055 40 8.0 6 500
## 2241 4.412739 41 8.0 6 500
## 2242 4.458927 42 8.0 6 500
## 2243 4.455172 43 8.0 6 500
## 2244 4.472907 44 8.0 6 500
## 2245 4.487183 45 8.0 6 500
## 2246 4.530004 46 8.0 6 500
## 2247 4.545669 47 8.0 6 500
## 2248 4.531172 48 8.0 6 500
## 2249 4.537452 49 8.0 6 500
## 2250 4.580452 50 8.0 6 500#revisando 2
train## TARGET VAR1 VAR2 VAR3 VAR4
## 1 0.127681 1 0.50 1 300
## 2 0.137929 2 0.50 1 300
## 3 0.166577 3 0.50 1 300
## 4 0.276655 4 0.50 1 300
## 5 0.375599 5 0.50 1 300
## 6 0.537873 6 0.50 1 300
## 7 0.690710 7 0.50 1 300
## 8 0.925325 8 0.50 1 300
## 9 1.144538 9 0.50 1 300
## 10 1.355085 10 0.50 1 300
## 11 1.580086 11 0.50 1 300
## 12 1.830346 12 0.50 1 300
## 13 2.056733 13 0.50 1 300
## 14 2.236009 14 0.50 1 300
## 15 2.364335 15 0.50 1 300
## 16 2.528628 16 0.50 1 300
## 17 2.689246 17 0.50 1 300
## 18 2.793072 18 0.50 1 300
## 19 2.902096 19 0.50 1 300
## 20 2.979681 20 0.50 1 300
## 21 3.067657 21 0.50 1 300
## 22 3.170272 22 0.50 1 300
## 23 3.216265 23 0.50 1 300
## 24 3.309949 24 0.50 1 300
## 25 3.391611 25 0.50 1 300
## 26 3.417622 26 0.50 1 300
## 27 3.455978 27 0.50 1 300
## 28 3.536280 28 0.50 1 300
## 29 3.570007 29 0.50 1 300
## 30 3.629901 30 0.50 1 300
## 31 3.687471 31 0.50 1 300
## 32 3.740705 32 0.50 1 300
## 33 3.753948 33 0.50 1 300
## 34 3.796698 34 0.50 1 300
## 35 3.863725 35 0.50 1 300
## 36 3.848697 36 0.50 1 300
## 37 3.904475 37 0.50 1 300
## 38 3.965479 38 0.50 1 300
## 39 3.969196 39 0.50 1 300
## 40 3.957788 40 0.50 1 300
## 41 3.992680 41 0.50 1 300
## 42 4.015803 42 0.50 1 300
## 43 4.042187 43 0.50 1 300
## 44 4.048160 44 0.50 1 300
## 45 4.094048 45 0.50 1 300
## 46 4.078181 46 0.50 1 300
## 47 4.107186 47 0.50 1 300
## 48 4.117277 48 0.50 1 300
## 49 4.125273 49 0.50 1 300
## 50 4.171190 50 0.50 1 300
## 51 0.155878 1 0.75 1 300
## 52 0.170812 2 0.75 1 300
## 53 0.187288 3 0.75 1 300
## 54 0.248702 4 0.75 1 300
## 55 0.382713 5 0.75 1 300
## 56 0.557819 6 0.75 1 300
## 57 0.749109 7 0.75 1 300
## 58 0.938826 8 0.75 1 300
## 59 1.236471 9 0.75 1 300
## 60 1.429025 10 0.75 1 300
## 61 1.695677 11 0.75 1 300
## 62 2.017155 12 0.75 1 300
## 63 2.147315 13 0.75 1 300
## 64 2.373673 14 0.75 1 300
## 65 2.431090 15 0.75 1 300
## 66 2.709494 16 0.75 1 300
## 67 2.816539 17 0.75 1 300
## 68 2.898707 18 0.75 1 300
## 69 3.019583 19 0.75 1 300
## 70 3.085324 20 0.75 1 300
## 71 3.203573 21 0.75 1 300
## 72 3.295130 22 0.75 1 300
## 73 3.382906 23 0.75 1 300
## 74 3.432251 24 0.75 1 300
## 75 3.498225 25 0.75 1 300
## 76 3.572614 26 0.75 1 300
## 77 3.676725 27 0.75 1 300
## 78 3.716630 28 0.75 1 300
## 79 3.762200 29 0.75 1 300
## 80 3.822481 30 0.75 1 300
## 81 3.799804 31 0.75 1 300
## 82 3.924902 32 0.75 1 300
## 83 3.979974 33 0.75 1 300
## 84 3.937622 34 0.75 1 300
## 85 3.993893 35 0.75 1 300
## 86 3.987886 36 0.75 1 300
## 87 4.089963 37 0.75 1 300
## 88 4.093614 38 0.75 1 300
## 89 4.139093 39 0.75 1 300
## 90 4.110661 40 0.75 1 300
## 91 4.217352 41 0.75 1 300
## 92 4.195063 42 0.75 1 300
## 93 4.226956 43 0.75 1 300
## 94 4.239293 44 0.75 1 300
## 95 4.257580 45 0.75 1 300
## 96 4.288147 46 0.75 1 300
## 97 4.286418 47 0.75 1 300
## 98 4.315307 48 0.75 1 300
## 99 4.300486 49 0.75 1 300
## 100 4.334072 50 0.75 1 300
## 101 0.166309 1 1.00 1 300
## 102 0.170795 2 1.00 1 300
## 103 0.202058 3 1.00 1 300
## 104 0.274991 4 1.00 1 300
## 105 0.418473 5 1.00 1 300
## 106 0.567888 6 1.00 1 300
## 107 0.772376 7 1.00 1 300
## 108 1.087661 8 1.00 1 300
## 109 1.312991 9 1.00 1 300
## 110 1.551817 10 1.00 1 300
## 111 1.763939 11 1.00 1 300
## 112 2.017992 12 1.00 1 300
## 113 2.219783 13 1.00 1 300
## 114 2.392901 14 1.00 1 300
## 115 2.595719 15 1.00 1 300
## 116 2.699901 16 1.00 1 300
## 117 2.914978 17 1.00 1 300
## 118 3.058510 18 1.00 1 300
## 119 3.114574 19 1.00 1 300
## 120 3.227191 20 1.00 1 300
## 121 3.330593 21 1.00 1 300
## 122 3.420758 22 1.00 1 300
## 123 3.510394 23 1.00 1 300
## 124 3.532449 24 1.00 1 300
## 125 3.537923 25 1.00 1 300
## 126 3.713953 26 1.00 1 300
## 127 3.768919 27 1.00 1 300
## 128 3.795615 28 1.00 1 300
## 129 3.826033 29 1.00 1 300
## 130 3.953023 30 1.00 1 300
## 131 3.916959 31 1.00 1 300
## 132 3.999019 32 1.00 1 300
## 133 4.047150 33 1.00 1 300
## 134 4.086897 34 1.00 1 300
## 135 4.096827 35 1.00 1 300
## 136 4.129370 36 1.00 1 300
## 137 4.177076 37 1.00 1 300
## 138 4.162246 38 1.00 1 300
## 139 4.204212 39 1.00 1 300
## 140 4.226510 40 1.00 1 300
## 141 4.236048 41 1.00 1 300
## 142 4.307272 42 1.00 1 300
## 143 4.335759 43 1.00 1 300
## 144 4.298464 44 1.00 1 300
## 145 4.375550 45 1.00 1 300
## 146 4.288603 46 1.00 1 300
## 147 4.364685 47 1.00 1 300
## 148 4.372191 48 1.00 1 300
## 149 4.385965 49 1.00 1 300
## 150 4.383493 50 1.00 1 300
## 151 0.175944 1 2.50 1 300
## 152 0.196573 2 2.50 1 300
## 153 0.247488 3 2.50 1 300
## 154 0.337059 4 2.50 1 300
## 155 0.437099 5 2.50 1 300
## 156 0.563077 6 2.50 1 300
## 157 0.825921 7 2.50 1 300
## 158 1.178394 8 2.50 1 300
## 159 1.382356 9 2.50 1 300
## 160 1.713704 10 2.50 1 300
## 161 1.873236 11 2.50 1 300
## 162 2.196796 12 2.50 1 300
## 163 2.377930 13 2.50 1 300
## 164 2.513315 14 2.50 1 300
## 165 2.708571 15 2.50 1 300
## 166 2.899908 16 2.50 1 300
## 167 3.033340 17 2.50 1 300
## 168 3.161268 18 2.50 1 300
## 169 3.229353 19 2.50 1 300
## 170 3.429312 20 2.50 1 300
## 171 3.572869 21 2.50 1 300
## 172 3.631798 22 2.50 1 300
## 173 3.769101 23 2.50 1 300
## 174 3.769031 24 2.50 1 300
## 175 3.814919 25 2.50 1 300
## 176 3.885927 26 2.50 1 300
## 177 3.950769 27 2.50 1 300
## 178 4.020379 28 2.50 1 300
## 179 4.137785 29 2.50 1 300
## 180 4.148262 30 2.50 1 300
## 181 4.203319 31 2.50 1 300
## 182 4.260490 32 2.50 1 300
## 183 4.227198 33 2.50 1 300
## 184 4.273755 34 2.50 1 300
## 185 4.306736 35 2.50 1 300
## 186 4.342187 36 2.50 1 300
## 187 4.368130 37 2.50 1 300
## 188 4.352696 38 2.50 1 300
## 189 4.372959 39 2.50 1 300
## 190 4.429294 40 2.50 1 300
## 191 4.438253 41 2.50 1 300
## 192 4.466243 42 2.50 1 300
## 193 4.464566 43 2.50 1 300
## 194 4.475949 44 2.50 1 300
## 195 4.485015 45 2.50 1 300
## 196 4.506570 46 2.50 1 300
## 197 4.527221 47 2.50 1 300
## 198 4.525439 48 2.50 1 300
## 199 4.526487 49 2.50 1 300
## 200 4.514927 50 2.50 1 300
## 201 0.182047 1 5.00 1 300
## 202 0.202994 2 5.00 1 300
## 203 0.263168 3 5.00 1 300
## 204 0.322585 4 5.00 1 300
## 205 0.494385 5 5.00 1 300
## 206 0.760753 6 5.00 1 300
## 207 0.859218 7 5.00 1 300
## 208 1.106689 8 5.00 1 300
## 209 1.482489 9 5.00 1 300
## 210 1.807238 10 5.00 1 300
## 211 2.026640 11 5.00 1 300
## 212 2.469547 12 5.00 1 300
## 213 2.554224 13 5.00 1 300
## 214 2.697837 14 5.00 1 300
## 215 2.863303 15 5.00 1 300
## 216 3.016358 16 5.00 1 300
## 217 3.198302 17 5.00 1 300
## 218 3.301550 18 5.00 1 300
## 219 3.294350 19 5.00 1 300
## 220 3.450587 20 5.00 1 300
## 221 3.525315 21 5.00 1 300
## 222 3.714139 22 5.00 1 300
## 223 3.820164 23 5.00 1 300
## 224 3.882769 24 5.00 1 300
## 225 3.928785 25 5.00 1 300
## 226 4.049111 26 5.00 1 300
## 227 4.167549 27 5.00 1 300
## 228 4.123427 28 5.00 1 300
## 229 4.124979 29 5.00 1 300
## 230 4.313931 30 5.00 1 300
## 231 4.359582 31 5.00 1 300
## 232 4.302964 32 5.00 1 300
## 233 4.446013 33 5.00 1 300
## 234 4.335103 34 5.00 1 300
## 235 4.410069 35 5.00 1 300
## 236 4.443310 36 5.00 1 300
## 237 4.432906 37 5.00 1 300
## 238 4.467562 38 5.00 1 300
## 239 4.490579 39 5.00 1 300
## 240 4.515247 40 5.00 1 300
## 241 4.510953 41 5.00 1 300
## 242 4.561429 42 5.00 1 300
## 243 4.534116 43 5.00 1 300
## 244 4.562100 44 5.00 1 300
## 245 4.561003 45 5.00 1 300
## 246 4.568826 46 5.00 1 300
## 247 4.553033 47 5.00 1 300
## 248 4.570040 48 5.00 1 300
## 249 4.573300 49 5.00 1 300
## 250 4.571814 50 5.00 1 300
## 251 0.189122 1 7.50 1 300
## 252 0.211250 2 7.50 1 300
## 253 0.250349 3 7.50 1 300
## 254 0.341312 4 7.50 1 300
## 255 0.474801 5 7.50 1 300
## 256 0.665814 6 7.50 1 300
## 257 0.937366 7 7.50 1 300
## 258 1.219604 8 7.50 1 300
## 259 1.636885 9 7.50 1 300
## 260 1.920938 10 7.50 1 300
## 261 2.243762 11 7.50 1 300
## 262 2.335715 12 7.50 1 300
## 263 2.638819 13 7.50 1 300
## 264 2.728014 14 7.50 1 300
## 265 2.913664 15 7.50 1 300
## 266 3.093713 16 7.50 1 300
## 267 3.087318 17 7.50 1 300
## 268 3.241346 18 7.50 1 300
## 269 3.466097 19 7.50 1 300
## 270 3.543466 20 7.50 1 300
## 271 3.639271 21 7.50 1 300
## 272 3.634343 22 7.50 1 300
## 273 3.757470 23 7.50 1 300
## 274 3.971733 24 7.50 1 300
## 275 3.908881 25 7.50 1 300
## 276 4.063816 26 7.50 1 300
## 277 4.101118 27 7.50 1 300
## 278 4.173140 28 7.50 1 300
## 279 4.270100 29 7.50 1 300
## 280 4.221576 30 7.50 1 300
## 281 4.268187 31 7.50 1 300
## 282 4.343852 32 7.50 1 300
## 283 4.308678 33 7.50 1 300
## 284 4.335706 34 7.50 1 300
## 285 4.439614 35 7.50 1 300
## 286 4.500960 36 7.50 1 300
## 287 4.456569 37 7.50 1 300
## 288 4.508501 38 7.50 1 300
## 289 4.508552 39 7.50 1 300
## 290 4.518178 40 7.50 1 300
## 291 4.531409 41 7.50 1 300
## 292 4.543959 42 7.50 1 300
## 293 4.536887 43 7.50 1 300
## 294 4.560964 44 7.50 1 300
## 295 4.549251 45 7.50 1 300
## 296 4.576568 46 7.50 1 300
## 297 4.571345 47 7.50 1 300
## 298 4.587596 48 7.50 1 300
## 299 4.598879 49 7.50 1 300
## 300 4.582874 50 7.50 1 300
## 301 0.179750 1 10.00 1 300
## 302 0.217125 2 10.00 1 300
## 303 0.249855 3 10.00 1 300
## 304 0.355806 4 10.00 1 300
## 305 0.546813 5 10.00 1 300
## 306 0.741229 6 10.00 1 300
## 307 0.995481 7 10.00 1 300
## 308 1.183519 8 10.00 1 300
## 309 1.485722 9 10.00 1 300
## 310 1.916004 10 10.00 1 300
## 311 2.108088 11 10.00 1 300
## 312 2.372253 12 10.00 1 300
## 313 2.501116 13 10.00 1 300
## 314 2.908499 14 10.00 1 300
## 315 2.998143 15 10.00 1 300
## 316 3.068110 16 10.00 1 300
## 317 3.191381 17 10.00 1 300
## 318 3.404978 18 10.00 1 300
## 319 3.588085 19 10.00 1 300
## 320 3.515001 20 10.00 1 300
## 321 3.698428 21 10.00 1 300
## 322 3.763575 22 10.00 1 300
## 323 3.955699 23 10.00 1 300
## 324 3.945359 24 10.00 1 300
## 325 4.122805 25 10.00 1 300
## 326 4.112151 26 10.00 1 300
## 327 4.294436 27 10.00 1 300
## 328 4.186343 28 10.00 1 300
## 329 4.208938 29 10.00 1 300
## 330 4.319469 30 10.00 1 300
## 331 4.384833 31 10.00 1 300
## 332 4.317023 32 10.00 1 300
## 333 4.385969 33 10.00 1 300
## 334 4.448834 34 10.00 1 300
## 335 4.507033 35 10.00 1 300
## 336 4.499606 36 10.00 1 300
## 337 4.512515 37 10.00 1 300
## 338 4.494781 38 10.00 1 300
## 339 4.525311 39 10.00 1 300
## 340 4.551597 40 10.00 1 300
## 341 4.527706 41 10.00 1 300
## 342 4.519658 42 10.00 1 300
## 343 4.536421 43 10.00 1 300
## 344 4.542274 44 10.00 1 300
## 345 4.547992 45 10.00 1 300
## 346 4.580697 46 10.00 1 300
## 347 4.561464 47 10.00 1 300
## 348 4.582219 48 10.00 1 300
## 349 4.599724 49 10.00 1 300
## 350 4.599142 50 10.00 1 300
## 351 0.164823 1 0.50 1 400
## 352 0.150012 2 0.50 1 400
## 353 0.178539 3 0.50 1 400
## 354 0.245690 4 0.50 1 400
## 355 0.346531 5 0.50 1 400
## 356 0.483991 6 0.50 1 400
## 357 0.683235 7 0.50 1 400
## 358 0.916402 8 0.50 1 400
## 359 1.044945 9 0.50 1 400
## 360 1.301461 10 0.50 1 400
## 361 1.531902 11 0.50 1 400
## 362 1.764613 12 0.50 1 400
## 363 1.990271 13 0.50 1 400
## 364 2.128724 14 0.50 1 400
## 365 2.328428 15 0.50 1 400
## 366 2.441326 16 0.50 1 400
## 367 2.574648 17 0.50 1 400
## 368 2.681760 18 0.50 1 400
## 369 2.788012 19 0.50 1 400
## 370 2.886400 20 0.50 1 400
## 371 2.985324 21 0.50 1 400
## 372 3.035272 22 0.50 1 400
## 373 3.179356 23 0.50 1 400
## 374 3.210933 24 0.50 1 400
## 375 3.265794 25 0.50 1 400
## 376 3.366810 26 0.50 1 400
## 377 3.395580 27 0.50 1 400
## 378 3.475887 28 0.50 1 400
## 379 3.554642 29 0.50 1 400
## 380 3.544800 30 0.50 1 400
## 381 3.618322 31 0.50 1 400
## 382 3.662204 32 0.50 1 400
## 383 3.723688 33 0.50 1 400
## 384 3.708152 34 0.50 1 400
## 385 3.710977 35 0.50 1 400
## 386 3.821240 36 0.50 1 400
## 387 3.863370 37 0.50 1 400
## 388 3.872764 38 0.50 1 400
## 389 3.927964 39 0.50 1 400
## 390 3.941831 40 0.50 1 400
## 391 3.954698 41 0.50 1 400
## 392 3.945361 42 0.50 1 400
## 393 3.986803 43 0.50 1 400
## 394 4.028922 44 0.50 1 400
## 395 4.047292 45 0.50 1 400
## 396 4.045737 46 0.50 1 400
## 397 4.053976 47 0.50 1 400
## 398 4.100175 48 0.50 1 400
## 399 4.076950 49 0.50 1 400
## 400 4.081247 50 0.50 1 400
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## 1993 4.458724 43 5.00 3 500
## 1994 4.423314 44 5.00 3 500
## 1995 4.461478 45 5.00 3 500
## 1996 4.488948 46 5.00 3 500
## 1997 4.535971 47 5.00 3 500
## 1998 4.532367 48 5.00 3 500
## 1999 4.511699 49 5.00 3 500
## 2000 4.536663 50 5.00 3 500
## 2001 0.308872 1 7.50 3 500
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## 2009 1.451715 9 7.50 3 500
## 2010 1.529654 10 7.50 3 500
## 2011 1.925566 11 7.50 3 500
## 2012 2.039799 12 7.50 3 500
## 2013 2.280059 13 7.50 3 500
## 2014 2.572122 14 7.50 3 500
## 2015 2.773450 15 7.50 3 500
## 2016 2.849104 16 7.50 3 500
## 2017 2.884715 17 7.50 3 500
## 2018 3.133778 18 7.50 3 500
## 2019 3.176235 19 7.50 3 500
## 2020 3.342446 20 7.50 3 500
## 2021 3.422575 21 7.50 3 500
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## 3041 4.415201 41 5.00 6 500
## 3042 4.425794 42 5.00 6 500
## 3043 4.414118 43 5.00 6 500
## 3044 4.450603 44 5.00 6 500
## 3045 4.457959 45 5.00 6 500
## 3046 4.436480 46 5.00 6 500
## 3047 4.451812 47 5.00 6 500
## 3048 4.474071 48 5.00 6 500
## 3049 4.460119 49 5.00 6 500
## 3050 4.514700 50 5.00 6 500
## 3051 0.313695 1 7.50 6 500
## 3052 0.302787 2 7.50 6 500
## 3053 0.335536 3 7.50 6 500
## 3054 0.381409 4 7.50 6 500
## 3055 0.526059 5 7.50 6 500
## 3056 0.599840 6 7.50 6 500
## 3057 0.917345 7 7.50 6 500
## 3058 1.235441 8 7.50 6 500
## 3059 1.325605 9 7.50 6 500
## 3060 1.525905 10 7.50 6 500
## 3061 1.906795 11 7.50 6 500
## 3062 2.056464 12 7.50 6 500
## 3063 2.314912 13 7.50 6 500
## 3064 2.529459 14 7.50 6 500
## 3065 2.581399 15 7.50 6 500
## 3066 2.823340 16 7.50 6 500
## 3067 2.980667 17 7.50 6 500
## 3068 3.154215 18 7.50 6 500
## 3069 3.262172 19 7.50 6 500
## 3070 3.421162 20 7.50 6 500
## 3071 3.545639 21 7.50 6 500
## 3072 3.404068 22 7.50 6 500
## 3073 3.540302 23 7.50 6 500
## 3074 3.746604 24 7.50 6 500
## 3075 3.742167 25 7.50 6 500
## 3076 3.972894 26 7.50 6 500
## 3077 3.898444 27 7.50 6 500
## 3078 4.057490 28 7.50 6 500
## 3079 4.120825 29 7.50 6 500
## 3080 4.136369 30 7.50 6 500
## 3081 4.131372 31 7.50 6 500
## 3082 4.198680 32 7.50 6 500
## 3083 4.265324 33 7.50 6 500
## 3084 4.321390 34 7.50 6 500
## 3085 4.309349 35 7.50 6 500
## 3086 4.353051 36 7.50 6 500
## 3087 4.433533 37 7.50 6 500
## 3088 4.402960 38 7.50 6 500
## 3089 4.429327 39 7.50 6 500
## 3090 4.382761 40 7.50 6 500
## 3091 4.404626 41 7.50 6 500
## 3092 4.465013 42 7.50 6 500
## 3093 4.478625 43 7.50 6 500
## 3094 4.473014 44 7.50 6 500
## 3095 4.460567 45 7.50 6 500
## 3096 4.490640 46 7.50 6 500
## 3097 4.511612 47 7.50 6 500
## 3098 4.522027 48 7.50 6 500
## 3099 4.535542 49 7.50 6 500
## 3100 4.530681 50 7.50 6 500
## 3101 0.318614 1 10.00 6 500
## 3102 0.325977 2 10.00 6 500
## 3103 0.341795 3 10.00 6 500
## 3104 0.468387 4 10.00 6 500
## 3105 0.495201 5 10.00 6 500
## 3106 0.607441 6 10.00 6 500
## 3107 0.743964 7 10.00 6 500
## 3108 1.183277 8 10.00 6 500
## 3109 1.316462 9 10.00 6 500
## 3110 1.647497 10 10.00 6 500
## 3111 1.862425 11 10.00 6 500
## 3112 2.110528 12 10.00 6 500
## 3113 2.340786 13 10.00 6 500
## 3114 2.453965 14 10.00 6 500
## 3115 2.822794 15 10.00 6 500
## 3116 2.872795 16 10.00 6 500
## 3117 3.036153 17 10.00 6 500
## 3118 3.105157 18 10.00 6 500
## 3119 3.322679 19 10.00 6 500
## 3120 3.300666 20 10.00 6 500
## 3121 3.343443 21 10.00 6 500
## 3122 3.590290 22 10.00 6 500
## 3123 3.579508 23 10.00 6 500
## 3124 3.725198 24 10.00 6 500
## 3125 3.875315 25 10.00 6 500
## 3126 3.860924 26 10.00 6 500
## 3127 4.065354 27 10.00 6 500
## 3128 4.000759 28 10.00 6 500
## 3129 4.072349 29 10.00 6 500
## 3130 4.160581 30 10.00 6 500
## 3131 4.188292 31 10.00 6 500
## 3132 4.218178 32 10.00 6 500
## 3133 4.254704 33 10.00 6 500
## 3134 4.250740 34 10.00 6 500
## 3135 4.441484 35 10.00 6 500
## 3136 4.412630 36 10.00 6 500
## 3137 4.406049 37 10.00 6 500
## 3138 4.451667 38 10.00 6 500
## 3139 4.409705 39 10.00 6 500
## 3140 4.468426 40 10.00 6 500
## 3141 4.456007 41 10.00 6 500
## 3142 4.503777 42 10.00 6 500
## 3143 4.468844 43 10.00 6 500
## 3144 4.497187 44 10.00 6 500
## 3145 4.516827 45 10.00 6 500
## 3146 4.509970 46 10.00 6 500
## 3147 4.525866 47 10.00 6 500
## 3148 4.527007 48 10.00 6 500
## 3149 4.541818 49 10.00 6 500
## 3150 4.533594 50 10.00 6 5002.3 Conjunto de Datos, Construcción
Combinado <- rbind(test,train)
str(Combinado)## 'data.frame': 5400 obs. of 5 variables:
## $ TARGET: num 0.153 0.164 0.183 0.291 0.402 ...
## $ VAR1 : int 1 2 3 4 5 6 7 8 9 10 ...
## $ VAR2 : num 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 ...
## $ VAR3 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ VAR4 : int 300 300 300 300 300 300 300 300 300 300 ...Inspección Datos Combinados
summary(Combinado)## TARGET VAR1 VAR2 VAR3 VAR4
## Min. :0.1277 Min. : 1.0 Min. : 0.500 Min. :1.000 Min. :300
## 1st Qu.:2.2517 1st Qu.:13.0 1st Qu.: 0.950 1st Qu.:1.000 1st Qu.:300
## Median :3.6129 Median :25.5 Median : 3.250 Median :3.000 Median :400
## Mean :3.0982 Mean :25.5 Mean : 4.004 Mean :3.333 Mean :400
## 3rd Qu.:4.2307 3rd Qu.:38.0 3rd Qu.: 6.375 3rd Qu.:6.000 3rd Qu.:500
## Max. :4.6191 Max. :50.0 Max. :10.000 Max. :6.000 Max. :500head(Combinado, 8)## TARGET VAR1 VAR2 VAR3 VAR4
## 1 0.153415 1 0.8 1 300
## 2 0.164236 2 0.8 1 300
## 3 0.182893 3 0.8 1 300
## 4 0.290766 4 0.8 1 300
## 5 0.402376 5 0.8 1 300
## 6 0.544483 6 0.8 1 300
## 7 0.691457 7 0.8 1 300
## 8 0.960261 8 0.8 1 300tail(Combinado, 8)## TARGET VAR1 VAR2 VAR3 VAR4
## 5393 4.468844 43 10 6 500
## 5394 4.497187 44 10 6 500
## 5395 4.516827 45 10 6 500
## 5396 4.509970 46 10 6 500
## 5397 4.525866 47 10 6 500
## 5398 4.527007 48 10 6 500
## 5399 4.541818 49 10 6 500
## 5400 4.533594 50 10 6 500dim(Combinado)## [1] 5400 5sum(complete.cases(Combinado))## [1] 54002.4 Análisis Gráfico de Cada Variable
Crearemos gráficos de dispersión para cada una de las variables independientes y analizamos su relación con TARGET
View(Combinado)
attach(Combinado)
names(Combinado)## [1] "TARGET" "VAR1" "VAR2" "VAR3" "VAR4"VAR1
VAR1 = ggplot(Combinado,aes(VAR1, TARGET))
VAR1 + geom_point() 
VAR2
VAR2 = ggplot(Combinado,aes(VAR2, TARGET))
VAR2 + geom_point() 
VAR3
VAR3 = ggplot(Combinado,aes(VAR3, TARGET))
VAR3 + geom_point() 
VAR4
VAR4 = ggplot(Combinado,aes(VAR4, TARGET))
VAR4 + geom_point() 
3.Modelización
Modelos seleccionados * Regresión lineal: capacidad para identificar relaciones lineales entre las variables predictoras y la variable respuesta. * Random forest: capacidad para manejar relaciones no lineales y capturar patrones complejos en los datos. * k-vecinos más cercanos (KNN): capacidad para identificar patrones locales y adaptarse a estructuras no lineales en los datos.
3.1 Regresión Lineal
Regresion <- lm(TARGET ~ VAR1 + VAR2 + VAR3 + VAR4, data = train)
summary(Regresion)##
## Call:
## lm(formula = TARGET ~ VAR1 + VAR2 + VAR3 + VAR4, data = train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.31196 -0.49367 0.09087 0.47884 1.00021
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.9167535 0.0552213 16.601 < 2e-16 ***
## VAR1 0.0868552 0.0006762 128.455 < 2e-16 ***
## VAR2 0.0574772 0.0028297 20.312 < 2e-16 ***
## VAR3 -0.0196577 0.0047486 -4.140 3.57e-05 ***
## VAR4 -0.0005762 0.0001195 -4.822 1.49e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5476 on 3145 degrees of freedom
## Multiple R-squared: 0.8435, Adjusted R-squared: 0.8433
## F-statistic: 4238 on 4 and 3145 DF, p-value: < 2.2e-16Modelo de Regresión Lineal Confiable.
plot(Regresion)
3.1.1 Predicción Regresión Lineal
Predicción sobre el dataset test.
Regresion_predict <- predict(Regresion, test)
head(Regresion_predict, n = 12)## 1 2 3 4 5 6 7 8
## 0.8570753 0.9439305 1.0307857 1.1176409 1.2044960 1.2913512 1.3782064 1.4650616
## 9 10 11 12
## 1.5519168 1.6387720 1.7256271 1.8124823summary(Regresion_predict)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6435 1.9862 3.0757 3.0747 4.1576 5.52683.1.2 Evaluación Regresión Lineal
Calculamos las diferencias cuadradas y coeficiente de determinación (R2)
R2 <- (Regresion_predict- test$TARGET)^2
R2_REGRESION <- 1 - sum((test$TARGET - Regresion_predict)^2) / sum((test$TARGET - mean(test$TARGET))^2)
cat("R2 para modelo de Regresion Lineal:", R2_REGRESION)## R2 para modelo de Regresion Lineal: 0.8408305Un coeficiente de determinación R2 de 0.84 sugiere que el modelo de regresión utilizado explica aproximadamente el 84% de la variabilidad en la variable dependiente. Este valor es bastante alto y podría indicar un buen ajuste del modelo a los datos. En términos analíticos, un R2 elevado como este implica que la mayoría de las fluctuaciones en la variable dependiente son capturadas y explicadas por las variables independientes en el modelo.
Calculamos la media de los errores al cuadrado (MSE)
MSE_REGRESION = mean(R2_REGRESION)
cat("MSE para modelo de Regresion Lineal:", MSE_REGRESION)## MSE para modelo de Regresion Lineal: 0.84083053.1.3 Distribución de Residuos
Residuos <- rstandard(Regresion)
ks.test(Residuos, "pnorm")##
## Asymptotic one-sample Kolmogorov-Smirnov test
##
## data: Residuos
## D = 0.094072, p-value < 2.2e-16
## alternative hypothesis: two-sidedshapiro.test(Residuos)##
## Shapiro-Wilk normality test
##
## data: Residuos
## W = 0.95004, p-value < 2.2e-16plot(Regresion, which = 1, pch = 16, col = "red", main = "Gráfico de Residuos")
plot(test$TARGET, Regresion_predict, main = "Valores Observados vs Valores Predichos", xlab = "Valores Observados", ylab = "Valores Predichos", pch = 16, col = "red")
3.2 Random Forest
library(randomForest)RandomF <- randomForest(TARGET ~ VAR1 + VAR2 + VAR3 + VAR4, data = train)
RANDOM_F = randomForest(x=train, y=train$TARGET, importance = TRUE)
plot(RANDOM_F)
3.2.1 Predicción Random Forest
RANDOMF_Predict <- predict(RANDOM_F, test)
head(RANDOMF_Predict, n=12)## 1 2 3 4 5 6 7 8
## 0.8257571 0.8269056 0.8307667 0.8541597 0.9012957 1.0123060 1.0928499 1.1940305
## 9 10 11 12
## 1.5238586 1.6425132 1.7709564 1.90625993.2.2 Evaluación
Calculamos las diferencias cuadradas y coeficiente de determinación (R2)
R2_RandomF <- (RANDOMF_Predict- test$TARGET)^2
R2_RF <- 1 - sum((test$TARGET - RANDOMF_Predict)^2) / sum((test$TARGET - mean(test$TARGET))^2)
cat("R2 para modelo de Random Forest:", R2_RF)## R2 para modelo de Random Forest: 0.9682242En el análisis de la base de datos utilizando el método Random Forest, se ha logrado obtener un coeficiente de determinación R2 significativamente elevado. Este resultado indica que el modelo Random Forest ha demostrado una capacidad excepcional para explicar la variabilidad en la variable dependiente en relación con las variables predictoras incluidas en la base de datos.
Calculamos la media de los errores al cuadrado (MSE)
MSE_RANDOMF = mean(R2_RandomF)
cat("MSE para modelo de Random Forest:", MSE_RANDOMF)## MSE para modelo de Random Forest: 0.062646233.3 KNN
set.seed(123)
library(caret)## Warning: package 'caret' was built under R version 4.3.2## Loading required package: latticeTRAINtarget.x <- train[,2:5]
head(TRAINtarget.x)## VAR1 VAR2 VAR3 VAR4
## 1 1 0.5 1 300
## 2 2 0.5 1 300
## 3 3 0.5 1 300
## 4 4 0.5 1 300
## 5 5 0.5 1 300
## 6 6 0.5 1 300TRAINtarget.y <- train$TARGET
head(TRAINtarget.y,n=5)## [1] 0.127681 0.137929 0.166577 0.276655 0.375599TESTtarget.x <- test[,2:5]
head(TESTtarget.x)## VAR1 VAR2 VAR3 VAR4
## 1 1 0.8 1 300
## 2 2 0.8 1 300
## 3 3 0.8 1 300
## 4 4 0.8 1 300
## 5 5 0.8 1 300
## 6 6 0.8 1 300TESTtarget.y <- test$TARGET
head(TESTtarget.y)## [1] 0.153415 0.164236 0.182893 0.290766 0.402376 0.544483TrainC <- trainControl(method = "repeatedcv", repeats = 3)
KNN <- train(y= TRAINtarget.y, x= TRAINtarget.x, method = "knn", trControl = TrainC)
KNN## k-Nearest Neighbors
##
## 3150 samples
## 4 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 2835, 2834, 2835, 2836, 2835, 2835, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 0.1130994 0.9936988 0.08383119
## 7 0.1222816 0.9925271 0.08953232
## 9 0.1153453 0.9932191 0.08112926
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 5.plot(KNN)
El valor final utilizado para el modelo fue k = 5. Se entiende que,
durante el proceso de modelado, el algoritmo consideró los cinco vecinos
más cercanos para realizar predicciones para cada punto de datos.
3.3.1 Predicción sobre Train
KNN_PREDICT <- predict(KNN, TRAINtarget.x)
head(KNN_PREDICT, n=10)## [1] 0.1517218 0.1547588 0.1941014 0.2685048 0.3982626 0.5459778 0.7350786
## [8] 0.9574120 1.1948820 1.41211023.3.2 Evaluación sobre Train
Calculamos las diferencias cuadradas y coeficiente de determinacion (R2) sobre Entrenamiento
library(MLmetrics)## Warning: package 'MLmetrics' was built under R version 4.3.2##
## Attaching package: 'MLmetrics'## The following objects are masked from 'package:caret':
##
## MAE, RMSE## The following object is masked from 'package:base':
##
## RecallR2_KNNTRAIN <- R2(TRAINtarget.x, KNN_PREDICT)
cat("R2 para modelo KNN:", R2_KNNTRAIN)## R2 para modelo KNN: 0.8277042 0.02192963 0.0009873177 0.0012055333.3.3 Prueba en Test
KNN_test <- train(y= TESTtarget.y, x=TESTtarget.x, method = "knn", trControl = TrainC)
KNN_test## k-Nearest Neighbors
##
## 2250 samples
## 4 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 2026, 2024, 2026, 2026, 2025, 2024, ...
## Resampling results across tuning parameters:
##
## k RMSE Rsquared MAE
## 5 0.10406063 0.9945338 0.07597543
## 7 0.08344126 0.9965022 0.06250394
## 9 0.07981254 0.9968211 0.05901757
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 9.plot(KNN_test)
3.3.4 Predicción sobre Test
TEST_PREDICTKNN <- predict(KNN, TESTtarget.x)
head(TEST_PREDICTKNN,n=10)## [1] 0.1582950 0.1645404 0.1950874 0.2740698 0.3966612 0.5590804 0.7417680
## [8] 0.9874784 1.2123702 1.45361503.3.5 Evaluación sobre Test
R2(TESTtarget.y, TEST_PREDICTKNN)## [1] 0.9985039#Calculamos la Métrica de MSE
KNN_MSE <- sqrt(mean((TESTtarget.y- TEST_PREDICTKNN)^2))
KNN_MSE## [1] 0.06215875cat("MSE:", KNN_MSE)## MSE: 0.062158754.Conclusión
Entre los modelos de regresión lineal, Random Forest y k-NN, se destaca un rendimiento superior en los modelos k-NN y Random Forest en cuanto a capacidad predictiva. Aunque el modelo de regresión lineal ofrece una interpretación clara de las relaciones lineales entre variables, su limitación para capturar patrones no lineales podría haber afectado su rendimiento al abordar la complejidad intrínseca de los datos.
Los modelos Random Forest y k-NN han demostrado una destacada habilidad para manejar relaciones no lineales y patrones más complejos. El Random Forest, al combinar múltiples árboles de decisión, exhibe una flexibilidad considerable en la modelización. Por otro lado, el k-NN, al considerar la proximidad de los vecinos, captura eficientemente variaciones locales en los datos.
Las métricas superiores obtenidas con k-NN y Random Forest sugieren que estos enfoques son más adecuados para el conjunto de datos analizado, proporcionando una capacidad predictiva robusta y capturando de manera efectiva las relaciones subyacentes. La elección entre k-NN y Random Forest podría depender de consideraciones específicas del problema, como la interpretabilidad del modelo o la sensibilidad a los valores atípicos, pero en términos generales, ambos modelos han demostrado ser opciones fuertes para este análisis en particular.