RF
Nancy Marroquín - A01198553
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 resulados para hacer predicciones más precisas y estables.
Instalar paquetes y llamar librerías
#install.packages("randomForest") #Bosques aleatorios
library(randomForest)## randomForest 4.7-1.2## Type rfNews() to see new features/changes/bug fixes.#install.packages("caret")
library(caret)## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following object is masked from 'package:randomForest':
##
## margin## Loading required package: latticeImportar base de datos
df <- read.csv ("C:\\Users\\lenovo\\Downloads\\House Prices.csv")Entender 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_prueba <- confusionMatrix(evaluacion_prueba, prueba$SalePrice)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