Bosque Aleatorio

Teoría

El bosque aleatorio es un algoritmo de aprendizaje autonático que combina el resultado de múltiples árboles de decisión para llegar a un resultado óptimo

Ejemplo1. Melbourne

En la base de datos tenemos los precios de más de 13,000 casas de la ciudad de Melbourne

Instalar paquetes y llamar líbrerias

# install.packages("tidyverse")
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
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# install.packages("rpart")
library(rpart)
# install.packages("rpart.plot")
library(rpart.plot)
# install.packages("randomForest")
library(randomForest)

## randomForest 4.7-1.2
## Type rfNews() to see new features/changes/bug fixes.
## 
## Attaching package: 'randomForest'
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# install.packages("modelr") #Calcular errores
library(modelr)
# install.packages("caret")
library(caret)

## Loading required package: lattice
## 
## Attaching package: 'caret'
## 
## The following object is masked from 'package:purrr':
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Importar base de datos

df <- read.csv("/Users/mariajoseflores/Downloads/melbourne.csv")

Entender la base de datos

summary(df)

##     Suburb            Address              Rooms            Type          
##  Length:13580       Length:13580       Min.   : 1.000   Length:13580      
##  Class :character   Class :character   1st Qu.: 2.000   Class :character  
##  Mode  :character   Mode  :character   Median : 3.000   Mode  :character  
##                                        Mean   : 2.938                     
##                                        3rd Qu.: 3.000                     
##                                        Max.   :10.000                     
##                                                                           
##      Price            Method            SellerG              Date          
##  Min.   :  85000   Length:13580       Length:13580       Length:13580      
##  1st Qu.: 650000   Class :character   Class :character   Class :character  
##  Median : 903000   Mode  :character   Mode  :character   Mode  :character  
##  Mean   :1075684                                                           
##  3rd Qu.:1330000                                                           
##  Max.   :9000000                                                           
##                                                                            
##     Distance        Postcode       Bedroom2         Bathroom    
##  Min.   : 0.00   Min.   :3000   Min.   : 0.000   Min.   :0.000  
##  1st Qu.: 6.10   1st Qu.:3044   1st Qu.: 2.000   1st Qu.:1.000  
##  Median : 9.20   Median :3084   Median : 3.000   Median :1.000  
##  Mean   :10.14   Mean   :3105   Mean   : 2.915   Mean   :1.534  
##  3rd Qu.:13.00   3rd Qu.:3148   3rd Qu.: 3.000   3rd Qu.:2.000  
##  Max.   :48.10   Max.   :3977   Max.   :20.000   Max.   :8.000  
##                                                                 
##       Car           Landsize         BuildingArea     YearBuilt   
##  Min.   : 0.00   Min.   :     0.0   Min.   :    0   Min.   :1196  
##  1st Qu.: 1.00   1st Qu.:   177.0   1st Qu.:   93   1st Qu.:1940  
##  Median : 2.00   Median :   440.0   Median :  126   Median :1970  
##  Mean   : 1.61   Mean   :   558.4   Mean   :  152   Mean   :1965  
##  3rd Qu.: 2.00   3rd Qu.:   651.0   3rd Qu.:  174   3rd Qu.:1999  
##  Max.   :10.00   Max.   :433014.0   Max.   :44515   Max.   :2018  
##  NA's   :62                         NA's   :6450    NA's   :5375  
##  CouncilArea          Lattitude        Longtitude     Regionname       
##  Length:13580       Min.   :-38.18   Min.   :144.4   Length:13580      
##  Class :character   1st Qu.:-37.86   1st Qu.:144.9   Class :character  
##  Mode  :character   Median :-37.80   Median :145.0   Mode  :character  
##                     Mean   :-37.81   Mean   :145.0                     
##                     3rd Qu.:-37.76   3rd Qu.:145.1                     
##                     Max.   :-37.41   Max.   :145.5                     
##                                                                        
##  Propertycount  
##  Min.   :  249  
##  1st Qu.: 4380  
##  Median : 6555  
##  Mean   : 7454  
##  3rd Qu.:10331  
##  Max.   :21650  
## 

str(df)

## 'data.frame':    13580 obs. of  21 variables:
##  $ Suburb       : chr  "Abbotsford" "Abbotsford" "Abbotsford" "Abbotsford" ...
##  $ Address      : chr  "85 Turner St" "25 Bloomburg St" "5 Charles St" "40 Federation La" ...
##  $ Rooms        : int  2 2 3 3 4 2 3 2 1 2 ...
##  $ Type         : chr  "h" "h" "h" "h" ...
##  $ Price        : num  1480000 1035000 1465000 850000 1600000 ...
##  $ Method       : chr  "S" "S" "SP" "PI" ...
##  $ SellerG      : chr  "Biggin" "Biggin" "Biggin" "Biggin" ...
##  $ Date         : chr  "3/12/2016" "4/02/2016" "4/03/2017" "4/03/2017" ...
##  $ Distance     : num  2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 ...
##  $ Postcode     : num  3067 3067 3067 3067 3067 ...
##  $ Bedroom2     : num  2 2 3 3 3 2 4 2 1 3 ...
##  $ Bathroom     : num  1 1 2 2 1 1 2 1 1 1 ...
##  $ Car          : num  1 0 0 1 2 0 0 2 1 2 ...
##  $ Landsize     : num  202 156 134 94 120 181 245 256 0 220 ...
##  $ BuildingArea : num  NA 79 150 NA 142 NA 210 107 NA 75 ...
##  $ YearBuilt    : num  NA 1900 1900 NA 2014 ...
##  $ CouncilArea  : chr  "Yarra" "Yarra" "Yarra" "Yarra" ...
##  $ Lattitude    : num  -37.8 -37.8 -37.8 -37.8 -37.8 ...
##  $ Longtitude   : num  145 145 145 145 145 ...
##  $ Regionname   : chr  "Northern Metropolitan" "Northern Metropolitan" "Northern Metropolitan" "Northern Metropolitan" ...
##  $ Propertycount: num  4019 4019 4019 4019 4019 ...

df <- na.omit(df)

Árbol de desición

arbol <- rpart(Price ~ Rooms + Distance + Bedroom2 + Bathroom + Car + Landsize + BuildingArea + YearBuilt +  Propertycount, data= df )
plot(arbol, uniform=TRUE)
text(arbol, cex=.5)

predict(arbol, head(df))

##       2       3       5       7       8      10 
## 1095996 1562641 1070605 2422140 1095996 1095996

head(df$Price)

## [1] 1035000 1465000 1600000 1876000 1636000 1097000

prueba_arbol <- head(df)

mae_arbol <- mae(arbol, prueba_arbol)

Bosque Aleatorio

set.seed(123)
renglones_entrenamiento <- createDataPartition(df$Price, p=0.8, list=FALSE)
entrenamiento <- df[renglones_entrenamiento, ]
prueba <- df[-renglones_entrenamiento, ]

rf <- randomForest(Price ~ Rooms + Distance + Bedroom2 + Bathroom + Car + Landsize + BuildingArea + YearBuilt +  Propertycount, data= entrenamiento, ntree=500, mtry=3, importance=TRUE)

resultados_entrenamiento <- predict(rf, entrenamiento)
resultados_prueba <- predict(rf, prueba)

mae_rf <- mae(rf,prueba)

library(tibble)

resultados <- tibble(
  Modelo = c("Árbol de Decisión", "Bosque Aleatorio"),
  MAE    = c(mae_arbol, mae_rf)
)

print(resultados)

## # A tibble: 2 × 2
##   Modelo                MAE
##   <chr>               <dbl>
## 1 Árbol de Decisión 295863.
## 2 Bosque Aleatorio  213383.

Importar base de datos

df2 <- mtcars

Entender la base de datos

summary(df2)

##       mpg             cyl             disp             hp       
##  Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
##  1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
##  Median :19.20   Median :6.000   Median :196.3   Median :123.0  
##  Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
##  3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
##  Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
##       drat             wt             qsec             vs        
##  Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
##  1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
##  Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
##  Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
##  3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
##  Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
##        am              gear            carb      
##  Min.   :0.0000   Min.   :3.000   Min.   :1.000  
##  1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
##  Median :0.0000   Median :4.000   Median :2.000  
##  Mean   :0.4062   Mean   :3.688   Mean   :2.812  
##  3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
##  Max.   :1.0000   Max.   :5.000   Max.   :8.000

str(df2)

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

df2 <- na.omit(df2)

Árbol de desición

arbol_mtcars <- rpart(mpg ~ cyl + disp + hp + wt + drat + qsec + vs + am + gear + carb,
  data = df2)
plot(arbol_mtcars, uniform=TRUE)
text(arbol_mtcars, cex=.5)

predict(arbol_mtcars, head(df2))

##         Mazda RX4     Mazda RX4 Wag        Datsun 710    Hornet 4 Drive 
##          18.26429          18.26429          26.66364          18.26429 
## Hornet Sportabout           Valiant 
##          18.26429          18.26429

head(df2$mpg)

## [1] 21.0 21.0 22.8 21.4 18.7 18.1

prueba_arbol <- head(df2)

mae_arbol <- mae(arbol_mtcars, prueba_arbol)

Bosque Aleatorio

# Paquetes necesarios
library(caret)
library(randomForest)
library(rpart)
library(Metrics)

## 
## Attaching package: 'Metrics'

## The following objects are masked from 'package:caret':
## 
##     precision, recall

## The following objects are masked from 'package:modelr':
## 
##     mae, mape, mse, rmse

library(tibble)

set.seed(123)

# Usar SIEMPRE el mismo data frame (mtcars)
df2 <- mtcars

# 1) Partición 80/20 usando la variable objetivo de df2 (mpg)
idx <- createDataPartition(df2$mpg, p = 0.8, list = FALSE)
entrenamiento <- df2[idx, ]
prueba        <- df2[-idx, ]

# Verificación anti-NA (mtcars no tiene NA por defecto)
stopifnot(sum(!complete.cases(entrenamiento)) == 0)
stopifnot(sum(!complete.cases(prueba)) == 0)

# 2) Árbol de decisión (para comparar)
arbol <- rpart(
  mpg ~ cyl + disp + hp + wt + drat + qsec + vs + am + gear + carb,
  data = entrenamiento,
  method = "anova"
)
pred_arbol_test <- predict(arbol, newdata = prueba)
mae_arbol <- mae(prueba$mpg, pred_arbol_test)

# 3) Bosque Aleatorio
rf <- randomForest(
  mpg ~ cyl + disp + hp + wt + drat + qsec + vs + am + gear + carb,
  data = entrenamiento,
  ntree = 500,
  mtry = 3,
  importance = TRUE
)

# Predicciones correctas
pred_rf_train <- predict(rf, newdata = entrenamiento)
pred_rf_test  <- predict(rf, newdata = prueba)

# MAE correcto (real vs predicho)
mae_rf_train <- mae(entrenamiento$mpg, pred_rf_train)
mae_rf_test  <- mae(prueba$mpg,        pred_rf_test)

# 4) Tabla de resultados
resultados <- tibble(
  Modelo = c("Árbol de Decisión", "Bosque Aleatorio"),
  MAE    = c(mae_arbol, mae_rf_test)
)

print(resultados)

## # A tibble: 2 × 2
##   Modelo              MAE
##   <chr>             <dbl>
## 1 Árbol de Decisión  3.86
## 2 Bosque Aleatorio   1.84

# (Opcional) Importancia de variables
print(importance(rf))

##        %IncMSE IncNodePurity
## cyl  10.836474     138.14411
## disp 12.169848     168.71762
## hp   11.666492     153.87071
## wt   13.377273     206.25914
## drat  5.672988      51.17328
## qsec  2.712477      26.84286
## vs    3.484933      14.54776
## am    2.677142      17.53307
## gear  3.596966      26.97460
## carb  6.103495      20.68776