Random Forest para casas Boston
- Cargar datos
- Partir en datos entrenamiento y datos de validación
- Crear modelo random forest
- Validar modelo
- Realizar predicciones
Cargar librerías
library(randomForest) # PAra randomforest
library(caret) # Para hce divisiones
library(readr)
library(dplyr)
library(reshape) # Para renombrar columnas
Cargar los datos
cat("Directorio de trabajo", getwd())
## Directorio de trabajo C:/Users/Usuario/Documents/Mis clases ITD/Semestre Enero Junio 2020/Analisis Inteligente de Datos/markdown
datos <- read.csv("../datos/BostonHousing.csv")
head(datos)
## CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT
## 1 0.00632 18 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98
## 2 0.02731 0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14
## 3 0.02729 0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03
## 4 0.03237 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94
## 5 0.06905 0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33
## 6 0.02985 0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21
## MEDV
## 1 24.0
## 2 21.6
## 3 34.7
## 4 33.4
## 5 36.2
## 6 28.7
Explorar datos
str(datos)
## 'data.frame': 506 obs. of 14 variables:
## $ CRIM : num 0.00632 0.02731 0.02729 0.03237 0.06905 ...
## $ ZN : num 18 0 0 0 0 0 12.5 12.5 12.5 12.5 ...
## $ INDUS : num 2.31 7.07 7.07 2.18 2.18 2.18 7.87 7.87 7.87 7.87 ...
## $ CHAS : int 0 0 0 0 0 0 0 0 0 0 ...
## $ NOX : num 0.538 0.469 0.469 0.458 0.458 0.458 0.524 0.524 0.524 0.524 ...
## $ RM : num 6.58 6.42 7.18 7 7.15 ...
## $ AGE : num 65.2 78.9 61.1 45.8 54.2 58.7 66.6 96.1 100 85.9 ...
## $ DIS : num 4.09 4.97 4.97 6.06 6.06 ...
## $ RAD : int 1 2 2 3 3 3 5 5 5 5 ...
## $ TAX : int 296 242 242 222 222 222 311 311 311 311 ...
## $ PTRATIO: num 15.3 17.8 17.8 18.7 18.7 18.7 15.2 15.2 15.2 15.2 ...
## $ B : num 397 397 393 395 397 ...
## $ LSTAT : num 4.98 9.14 4.03 2.94 5.33 ...
## $ MEDV : num 24 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 ...
summary(datos)
## CRIM ZN INDUS CHAS
## Min. : 0.00632 Min. : 0.00 Min. : 0.46 Min. :0.00000
## 1st Qu.: 0.08204 1st Qu.: 0.00 1st Qu.: 5.19 1st Qu.:0.00000
## Median : 0.25651 Median : 0.00 Median : 9.69 Median :0.00000
## Mean : 3.61352 Mean : 11.36 Mean :11.14 Mean :0.06917
## 3rd Qu.: 3.67708 3rd Qu.: 12.50 3rd Qu.:18.10 3rd Qu.:0.00000
## Max. :88.97620 Max. :100.00 Max. :27.74 Max. :1.00000
## NOX RM AGE DIS
## Min. :0.3850 Min. :3.561 Min. : 2.90 Min. : 1.130
## 1st Qu.:0.4490 1st Qu.:5.886 1st Qu.: 45.02 1st Qu.: 2.100
## Median :0.5380 Median :6.208 Median : 77.50 Median : 3.207
## Mean :0.5547 Mean :6.285 Mean : 68.57 Mean : 3.795
## 3rd Qu.:0.6240 3rd Qu.:6.623 3rd Qu.: 94.08 3rd Qu.: 5.188
## Max. :0.8710 Max. :8.780 Max. :100.00 Max. :12.127
## RAD TAX PTRATIO B
## Min. : 1.000 Min. :187.0 Min. :12.60 Min. : 0.32
## 1st Qu.: 4.000 1st Qu.:279.0 1st Qu.:17.40 1st Qu.:375.38
## Median : 5.000 Median :330.0 Median :19.05 Median :391.44
## Mean : 9.549 Mean :408.2 Mean :18.46 Mean :356.67
## 3rd Qu.:24.000 3rd Qu.:666.0 3rd Qu.:20.20 3rd Qu.:396.23
## Max. :24.000 Max. :711.0 Max. :22.00 Max. :396.90
## LSTAT MEDV
## Min. : 1.73 Min. : 5.00
## 1st Qu.: 6.95 1st Qu.:17.02
## Median :11.36 Median :21.20
## Mean :12.65 Mean :22.53
## 3rd Qu.:16.95 3rd Qu.:25.00
## Max. :37.97 Max. :50.00
- La variable predicitva es MEDV que significa el precio medio de venta de las casas
- Todas son numéricas
Hacer conjunto de entrenamiento y conjunto de validación
set.seed(2020)
entrena = createDataPartition(datos$MEDV, p=0.7, list = FALSE)
datos.Entrena = datos[entrena,]
datos.Valida = datos[-entrena,]
head(datos.Entrena, 15)
## CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B
## 1 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90
## 2 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90
## 3 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83
## 4 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63
## 5 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90
## 6 0.02985 0.0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12
## 7 0.08829 12.5 7.87 0 0.524 6.012 66.6 5.5605 5 311 15.2 395.60
## 8 0.14455 12.5 7.87 0 0.524 6.172 96.1 5.9505 5 311 15.2 396.90
## 9 0.21124 12.5 7.87 0 0.524 5.631 100.0 6.0821 5 311 15.2 386.63
## 10 0.17004 12.5 7.87 0 0.524 6.004 85.9 6.5921 5 311 15.2 386.71
## 12 0.11747 12.5 7.87 0 0.524 6.009 82.9 6.2267 5 311 15.2 396.90
## 13 0.09378 12.5 7.87 0 0.524 5.889 39.0 5.4509 5 311 15.2 390.50
## 14 0.62976 0.0 8.14 0 0.538 5.949 61.8 4.7075 4 307 21.0 396.90
## 15 0.63796 0.0 8.14 0 0.538 6.096 84.5 4.4619 4 307 21.0 380.02
## 16 0.62739 0.0 8.14 0 0.538 5.834 56.5 4.4986 4 307 21.0 395.62
## LSTAT MEDV
## 1 4.98 24.0
## 2 9.14 21.6
## 3 4.03 34.7
## 4 2.94 33.4
## 5 5.33 36.2
## 6 5.21 28.7
## 7 12.43 22.9
## 8 19.15 27.1
## 9 29.93 16.5
## 10 17.10 18.9
## 12 13.27 18.9
## 13 15.71 21.7
## 14 8.26 20.4
## 15 10.26 18.2
## 16 8.47 19.9
head(datos.Valida, 15)
## CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B
## 11 0.22489 12.5 7.87 0 0.524 6.377 94.3 6.3467 5 311 15.2 392.52
## 18 0.78420 0.0 8.14 0 0.538 5.990 81.7 4.2579 4 307 21.0 386.75
## 19 0.80271 0.0 8.14 0 0.538 5.456 36.6 3.7965 4 307 21.0 288.99
## 22 0.85204 0.0 8.14 0 0.538 5.965 89.2 4.0123 4 307 21.0 392.53
## 28 0.95577 0.0 8.14 0 0.538 6.047 88.8 4.4534 4 307 21.0 306.38
## 32 1.35472 0.0 8.14 0 0.538 6.072 100.0 4.1750 4 307 21.0 376.73
## 33 1.38799 0.0 8.14 0 0.538 5.950 82.0 3.9900 4 307 21.0 232.60
## 35 1.61282 0.0 8.14 0 0.538 6.096 96.9 3.7598 4 307 21.0 248.31
## 36 0.06417 0.0 5.96 0 0.499 5.933 68.2 3.3603 5 279 19.2 396.90
## 42 0.12744 0.0 6.91 0 0.448 6.770 2.9 5.7209 3 233 17.9 385.41
## 43 0.14150 0.0 6.91 0 0.448 6.169 6.6 5.7209 3 233 17.9 383.37
## 46 0.17142 0.0 6.91 0 0.448 5.682 33.8 5.1004 3 233 17.9 396.90
## 48 0.22927 0.0 6.91 0 0.448 6.030 85.5 5.6894 3 233 17.9 392.74
## 53 0.05360 21.0 5.64 0 0.439 6.511 21.1 6.8147 4 243 16.8 396.90
## 60 0.10328 25.0 5.13 0 0.453 5.927 47.2 6.9320 8 284 19.7 396.90
## LSTAT MEDV
## 11 20.45 15.0
## 18 14.67 17.5
## 19 11.69 20.2
## 22 13.83 19.6
## 28 17.28 14.8
## 32 13.04 14.5
## 33 27.71 13.2
## 35 20.34 13.5
## 36 9.68 18.9
## 42 4.84 26.6
## 43 5.81 25.3
## 46 10.21 19.3
## 48 18.80 16.6
## 53 5.28 25.0
## 60 9.22 19.6
Realizar el modelo de random Forest
- Contruir el bosque aleatrio
- Construye varios árboles y los proceso
- x son los predictores
- y es la variables de salida
- xtest son los predictores del conjutno de validción
- ytest es la variable predictiva del conjunto de validación
- importance se refeire a procesar de cada variable predictora
- keep forest significa si se queda con los árboles que se vayan generando para luego utilizarlos
modelo <- randomForest(x = datos.Entrena[,1:13], y = datos.Entrena[,14], ntree=1000, xtest = datos.Valida[,1:13], ytest = datos.Valida[,14], importance = TRUE, keep.forest = TRUE)
modelo
##
## Call:
## randomForest(x = datos.Entrena[, 1:13], y = datos.Entrena[, 14], xtest = datos.Valida[, 1:13], ytest = datos.Valida[, 14], ntree = 1000, importance = TRUE, keep.forest = TRUE)
## Type of random forest: regression
## Number of trees: 1000
## No. of variables tried at each split: 4
##
## Mean of squared residuals: 11.97907
## % Var explained: 86.49
## Test set MSE: 9.14
## % Var explained: 87.72
modelo$importance
## %IncMSE IncNodePurity
## CRIM 9.1209731 2014.4919
## ZN 0.6899801 249.6783
## INDUS 5.1589892 1287.9252
## CHAS 0.7447949 207.2129
## NOX 11.0733940 2458.6715
## RM 31.2493666 8230.4467
## AGE 3.9633101 866.2132
## DIS 7.6970953 1981.2095
## RAD 1.2821717 257.0120
## TAX 4.5394163 944.1180
## PTRATIO 6.7907138 1760.4709
## B 2.0575237 702.9492
## LSTAT 66.2273159 9997.7758
Graficando el modelo
plot(datos.Entrena$MEDV, predict(modelo, newdata = datos.Entrena[,]), xlab = "Actual", ylab="Predichos")
abline(0,1)
plot(datos.Valida$MEDV, predict(modelo, newdata = datos.Valida[,]), xlab = "Actual", ylab="Predichos")
abline(0,1)
- El gráfico y la linea de tendendencia indican que si es recomendable aplicar este modeolo de regresión de random forest para este conjunto de datos
- La decisión de la prediccón no la toma un sólo árbol sino que la toman todos los árboles generados aleatoriamente.
Prediccioes específicas
- Predicciones de las priemras seis
head(datos.Valida)
## CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B
## 11 0.22489 12.5 7.87 0 0.524 6.377 94.3 6.3467 5 311 15.2 392.52
## 18 0.78420 0.0 8.14 0 0.538 5.990 81.7 4.2579 4 307 21.0 386.75
## 19 0.80271 0.0 8.14 0 0.538 5.456 36.6 3.7965 4 307 21.0 288.99
## 22 0.85204 0.0 8.14 0 0.538 5.965 89.2 4.0123 4 307 21.0 392.53
## 28 0.95577 0.0 8.14 0 0.538 6.047 88.8 4.4534 4 307 21.0 306.38
## 32 1.35472 0.0 8.14 0 0.538 6.072 100.0 4.1750 4 307 21.0 376.73
## LSTAT MEDV
## 11 20.45 15.0
## 18 14.67 17.5
## 19 11.69 20.2
## 22 13.83 19.6
## 28 17.28 14.8
## 32 13.04 14.5
head(predict(modelo, newdata = datos.Valida[,]))
## 11 18 19 22 28 32
## 21.18267 17.97345 18.29781 18.73298 15.64002 18.57603