Random Forest
Romery Luna A01749837
2025-02-24
Teoría
Random Forest es un algoritmo de aprendizaje atomático supervisado que se usa para clasificar y/o hacerregresiones. Se basa en la creación de múltiples árboles de decisión y combina sus resultados para hcer predicciones más precisas y estables.
Importar paquetes y llamar librerías
#install.packages("randomForest")
library(randomForest)## randomForest 4.7-1.2## Type rfNews() to see new features/changes/bug fixes.#install.packages("caret")
library(caret)## Cargando paquete requerido: ggplot2##
## Adjuntando el paquete: 'ggplot2'## The following object is masked from 'package:randomForest':
##
## margin## Cargando paquete requerido: latticeImportar base de datos
df <- read.csv("C:\\Users\\rylun\\Downloads\\House Prices.csv")summary(df)## Id MSSubClass MSZoning LotArea
## Min. : 0.0 Min. : 20.00 Length:2919 Min. : 1300
## 1st Qu.: 729.5 1st Qu.: 20.00 Class :character 1st Qu.: 7478
## Median :1459.0 Median : 50.00 Mode :character Median : 9453
## Mean :1459.0 Mean : 57.14 Mean : 10168
## 3rd Qu.:2188.5 3rd Qu.: 70.00 3rd Qu.: 11570
## Max. :2918.0 Max. :190.00 Max. :215245
##
## LotConfig BldgType OverallCond YearBuilt
## Length:2919 Length:2919 Min. :1.000 Min. :1872
## Class :character Class :character 1st Qu.:5.000 1st Qu.:1954
## Mode :character Mode :character Median :5.000 Median :1973
## Mean :5.565 Mean :1971
## 3rd Qu.:6.000 3rd Qu.:2001
## Max. :9.000 Max. :2010
##
## YearRemodAdd Exterior1st BsmtFinSF2 TotalBsmtSF
## Min. :1950 Length:2919 Min. : 0.00 Min. : 0.0
## 1st Qu.:1965 Class :character 1st Qu.: 0.00 1st Qu.: 793.0
## Median :1993 Mode :character Median : 0.00 Median : 989.5
## Mean :1984 Mean : 49.58 Mean :1051.8
## 3rd Qu.:2004 3rd Qu.: 0.00 3rd Qu.:1302.0
## Max. :2010 Max. :1526.00 Max. :6110.0
## NA's :1 NA's :1
## SalePrice
## Min. : 34900
## 1st Qu.:129975
## Median :163000
## Mean :180921
## 3rd Qu.:214000
## Max. :755000
## NA's :1459head(df)## Id MSSubClass MSZoning LotArea LotConfig BldgType OverallCond YearBuilt
## 1 0 60 RL 8450 Inside 1Fam 5 2003
## 2 1 20 RL 9600 FR2 1Fam 8 1976
## 3 2 60 RL 11250 Inside 1Fam 5 2001
## 4 3 70 RL 9550 Corner 1Fam 5 1915
## 5 4 60 RL 14260 FR2 1Fam 5 2000
## 6 5 50 RL 14115 Inside 1Fam 5 1993
## YearRemodAdd Exterior1st BsmtFinSF2 TotalBsmtSF SalePrice
## 1 2003 VinylSd 0 856 208500
## 2 1976 MetalSd 0 1262 181500
## 3 2002 VinylSd 0 920 223500
## 4 1970 Wd Sdng 0 756 140000
## 5 2000 VinylSd 0 1145 250000
## 6 1995 VinylSd 0 796 143000str(df)## 'data.frame': 2919 obs. of 13 variables:
## $ Id : int 0 1 2 3 4 5 6 7 8 9 ...
## $ MSSubClass : int 60 20 60 70 60 50 20 60 50 190 ...
## $ MSZoning : chr "RL" "RL" "RL" "RL" ...
## $ LotArea : int 8450 9600 11250 9550 14260 14115 10084 10382 6120 7420 ...
## $ LotConfig : chr "Inside" "FR2" "Inside" "Corner" ...
## $ BldgType : chr "1Fam" "1Fam" "1Fam" "1Fam" ...
## $ OverallCond : int 5 8 5 5 5 5 5 6 5 6 ...
## $ YearBuilt : int 2003 1976 2001 1915 2000 1993 2004 1973 1931 1939 ...
## $ YearRemodAdd: int 2003 1976 2002 1970 2000 1995 2005 1973 1950 1950 ...
## $ Exterior1st : chr "VinylSd" "MetalSd" "VinylSd" "Wd Sdng" ...
## $ BsmtFinSF2 : int 0 0 0 0 0 0 0 32 0 0 ...
## $ TotalBsmtSF : int 856 1262 920 756 1145 796 1686 1107 952 991 ...
## $ SalePrice : int 208500 181500 223500 140000 250000 143000 307000 200000 129900 118000 ...df$MSZoning <- as.factor(df$MSZoning)
df$LotConfig <- as.factor(df$LotConfig)
df$BldgType <- as.factor(df$BldgType)
df$Exterior1st <- as.factor(df$Exterior1st)
df$SalePrice <- as.factor(df$SalePrice)
str(df)## 'data.frame': 2919 obs. of 13 variables:
## $ Id : int 0 1 2 3 4 5 6 7 8 9 ...
## $ MSSubClass : int 60 20 60 70 60 50 20 60 50 190 ...
## $ MSZoning : Factor w/ 6 levels "","C (all)","FV",..: 5 5 5 5 5 5 5 5 6 5 ...
## $ LotArea : int 8450 9600 11250 9550 14260 14115 10084 10382 6120 7420 ...
## $ LotConfig : Factor w/ 5 levels "Corner","CulDSac",..: 5 3 5 1 3 5 5 1 5 1 ...
## $ BldgType : Factor w/ 5 levels "1Fam","2fmCon",..: 1 1 1 1 1 1 1 1 1 2 ...
## $ OverallCond : int 5 8 5 5 5 5 5 6 5 6 ...
## $ YearBuilt : int 2003 1976 2001 1915 2000 1993 2004 1973 1931 1939 ...
## $ YearRemodAdd: int 2003 1976 2002 1970 2000 1995 2005 1973 1950 1950 ...
## $ Exterior1st : Factor w/ 16 levels "","AsbShng","AsphShn",..: 14 10 14 15 14 14 14 8 5 10 ...
## $ BsmtFinSF2 : int 0 0 0 0 0 0 0 32 0 0 ...
## $ TotalBsmtSF : int 856 1262 920 756 1145 796 1686 1107 952 991 ...
## $ SalePrice : Factor w/ 663 levels "34900","35311",..: 413 340 443 195 495 204 574 391 152 114 ...sum(is.na(df))## [1] 1461df <- na.omit(df)set.seed(123)
renglones_entrenamiento <- createDataPartition(df$SalePrice,p = 0.7,list = FALSE)## Warning in createDataPartition(df$SalePrice, p = 0.7, list = FALSE): Some
## classes have a single record ( 34900, 35311, 37900, 39300, 40000, 52000, 52500,
## 55993, 58500, 61000, 62383, 64500, 66500, 68400, 68500, 72500, 73000, 75000,
## 75500, 76000, 76500, 78000, 79500, 80500, 83500, 84000, 84900, 85400, 85500,
## 87500, 89000, 89471, 90350, 91300, 92000, 92900, 94000, 94500, 94750, 97500,
## 98300, 98600, 99500, 99900, 101000, 101800, 102776, 103000, 103200, 103600,
## 104000, 105500, 105900, 106250, 107400, 107900, 108480, 108500, 108959, 109008,
## 111000, 111250, 114504, 116050, 116500, 116900, 118400, 118858, 118964, 119200,
## 119750, 121000, 121500, 121600, 122900, 123500, 123600, 126175, 126500, 128200,
## 128900, 128950, 130250, 131400, 132250, 133500, 133700, 134432, 134450, 134800,
## 135750, 135900, 135960, 136900, 136905, 137450, 137900, 138800, 138887, 139500,
## 139600, 139900, 139950, 140200, 141500, 142125, 142600, 142953, 143250, 143750,
## 143900, 144152, 144900, 145250, 145500, 145900, 146500, 146800, 147400, 147500,
## 148800, 149300, 149350, 149700, 150500, 150750, 150900, 151400, 151500, 153337,
## 153575, 154300, 154500, 154900, 155835, 155900, 156500, 156932, 158500, 158900,
## 159434, 159895, 159950, 160200, 161750, 162500, 163900, 163990, 164700, 164900,
## 164990, 165150, 165400, 165600, 167240, 169900, 169990, 171500, 171750, 171900,
## 172000, 172400, 172785, 173500, 173733, 173900, 174500, 174900, 176432, 176485,
## 178400, 178740, 178900, 179400, 179500, 179540, 179600, 179665, 181134, 181500,
## 181900, 182000, 182900, 183000, 183200, 183500, 183900, 184100, 184750, 184900,
## 185500, 185750, 185850, 185900, 186000, 186700, 187100, 187750, 188500, 188700,
## 189950, 192140, 193879, 194201, 194700, 195400, 198500, 198900, 200100, 200141,
## 200624, 201800, 202665, 202900, 204750, 204900, 205950, 206000, 206300, 206900,
## 208300, 208500, 209500, 212900, 213250, 213490, 214500, 214900, 215200, 216000,
## 216500, 216837, 217500, 218000, 219210, 221500, 223000, 224500, 226700, 227680,
## 227875, 228950, 229000, 229456, 230500, 232600, 233000, 233170, 233230, 235128,
## 237500, 238000, 239500, 239686, 239799, 239900, 241000, 243000, 244400, 244600,
## 245000, 245350, 245500, 246578, 248328, 248900, 249700, 250580, 251000, 252000,
## 252678, 253000, 253293, 254000, 254900, 255500, 255900, 256300, 257000, 257500,
## 258000, 259000, 259500, 260400, 261500, 262000, 262280, 263000, 263435, 264132,
## 264561, 265000, 265900, 265979, 266500, 267000, 269500, 269790, 271900, 274000,
## 274300, 274725, 274900, 274970, 275500, 276000, 277500, 279500, 281000, 281213,
## 282922, 283463, 286000, 287090, 289000, 293077, 294000, 295000, 295493, 297000,
## 299800, 301000, 301500, 303477, 305000, 305900, 306000, 307000, 309000, 311500,
## 311872, 312500, 313000, 314813, 315500, 315750, 316600, 317000, 318061, 319000,
## 319900, 324000, 325300, 325624, 326000, 328000, 328900, 333168, 336000, 337000,
## 337500, 339750, 341000, 342643, 348000, 354000, 359100, 360000, 361919, 367294,
## 369900, 370878, 372402, 372500, 374000, 375000, 377426, 377500, 378500, 380000,
## 381000, 383970, 386250, 392000, 392500, 394432, 394617, 395000, 395192, 402000,
## 402861, 403000, 410000, 412500, 415298, 423000, 424870, 426000, 430000, 437154,
## 438780, 440000, 446261, 451950, 465000, 466500, 475000, 485000, 501837, 538000,
## 555000, 556581, 582933, 611657, 625000, 745000, 755000 ) and these will be
## selected for the sampleentrenamiento <- df[renglones_entrenamiento, ]
prueba <- df[-renglones_entrenamiento, ]
modelo <- randomForest(SalePrice ~ ., data = entrenamiento)
# ntree=100
print(modelo)##
## Call:
## randomForest(formula = SalePrice ~ ., data = entrenamiento)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 3
##
## OOB estimate of error rate: 99.23%
## Confusion matrix:
## 34900 35311 37900 39300 40000 52000 52500 55000 55993 58500 60000 61000
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## 62383 64500 66500 67000 68400 68500 72500 73000 75000 75500 76000 76500
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## [ reached getOption("max.print") -- omitted 513 rows ]evaluación_entrenamiento <- predict(modelo, entrenamiento)
evaluación_prueba <- predict(modelo,prueba)prediccion <- predict(modelo,prueba)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