Random Forest
Ana Paula Alvear Cantú
2025-02-24
Teoría
Random Forest es un algoritmo de aprendizaje automático supervisado que se usa para clasificar y/o hacer regresiones. Se basa en la creación de múltiples árboles de decisión y combina sus resultados para hacer predicciones mas precisas y estables.
Importar base de datos
df <- read.csv("/Users/anapaualvear/Downloads/House Prices.csv")Instalar paquetes y llamar librerías
#install.packages("randomForest")
library(randomForest)
library(caret) #Enrtenamiendo de MLEntender la base de datos
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$SalePrice <- as.numeric(df$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 : num 208500 181500 223500 140000 250000 ...df <- na.omit(df)Entrenar el modelo
set.seed(123)
renglones_entrenamiento <- createDataPartition(df$SalePrice, p= 0.7, list = FALSE)
entrenamiento <- df[renglones_entrenamiento, ]
prueba <- df [-renglones_entrenamiento, ]
modelo <- randomForest(SalePrice ~ ., data=entrenamiento)
print(modelo)##
## Call:
## randomForest(formula = SalePrice ~ ., data = entrenamiento)
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 4
##
## Mean of squared residuals: 1621295030
## % Var explained: 74.72Evaluar el modelo
evaluacion_entrenamiento <- predict(modelo, entrenamiento)
evaluacion_prueba <- predict(modelo, prueba)
#matriz_confusion_entrenamiento <- confusionMatrix(evaluacion_entrenamiento, entrenamiento$SalePrice)
#matriz_confusion_entrenamiento <- confusionMatrix(evaluacion_prueba, prueba$SalePrice)Generar predicciones
prediccion <- predict(modelo, prueba)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