As principais referências para a produção deste exemplo foram http://cran.r-project.org/doc/contrib/Zhao_R_and_data_mining.pdf, http://cran.r-project.org/web/packages/randomForest/randomForest.pdf e ftp://131.252.97.79/Transfer/temp_treg/WFRE_Articles/Liaw_02_Classification%20and%20regression%20by%20randomForest.pdf
Carga da biblioteca e do conjunto de dados utilizados no exemplo:
library(randomForest)
data(iris)
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
Etapa de treinamento:
forestIris <- randomForest(Species ~ Petal.Width + Petal.Length, data = iris,
prox = TRUE)
forestIris
##
## Call:
## randomForest(formula = Species ~ Petal.Width + Petal.Length, data = iris, prox = TRUE)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 1
##
## OOB estimate of error rate: 3.33%
## Confusion matrix:
## setosa versicolor virginica class.error
## setosa 50 0 0 0.00
## versicolor 0 47 3 0.06
## virginica 0 2 48 0.04
Recuperando uma das árvores geradas:
getTree(forestIris, k = 2)
## left daughter right daughter split var split point status prediction
## 1 2 3 1 0.80 1 0
## 2 0 0 0 0.00 -1 1
## 3 4 5 2 4.75 1 0
## 4 6 7 1 1.65 1 0
## 5 8 9 1 1.75 1 0
## 6 0 0 0 0.00 -1 2
## 7 0 0 0 0.00 -1 3
## 8 10 11 2 4.95 1 0
## 9 0 0 0 0.00 -1 3
## 10 0 0 0 0.00 -1 2
## 11 12 13 2 5.35 1 0
## 12 14 15 2 5.05 1 0
## 13 0 0 0 0.00 -1 3
## 14 0 0 0 0.00 -1 3
## 15 0 0 0 0.00 -1 2
Gerando uma matriz de confusão:
table(predict(forestIris), iris$Species)
##
## setosa versicolor virginica
## setosa 50 0 0
## versicolor 0 47 2
## virginica 0 3 48
prop.table(table(predict(forestIris), iris$Species))
##
## setosa versicolor virginica
## setosa 0.33333 0.00000 0.00000
## versicolor 0.00000 0.31333 0.01333
## virginica 0.00000 0.02000 0.32000
Identificação dos centros das classes:
iris.p <- classCenter(iris[, c(3, 4)], iris$Species, forestIris$prox)
plot(iris[, 3], iris[, 4], pch = 21, xlab = names(iris)[3], ylab = names(iris)[4],
bg = c("red", "blue", "green")[as.numeric(factor(iris$Species))], main = "Dados sobre as plantas Iris com os centros das classes")
points(iris.p[, 1], iris.p[, 2], pch = 21, cex = 2, bg = c("red", "blue", "green"))
Combinando vários modelos:
forestIris1 <- randomForest(Species ~ Petal.Width + Petal.Length, data = iris,
prox = TRUE, ntree = 50)
forestIris2 <- randomForest(Species ~ Petal.Width + Petal.Length, data = iris,
prox = TRUE, ntree = 50)
forestIris3 <- randomForest(Species ~ Petal.Width + Petal.Length, data = iris,
prox = TRUE, ntree = 50)
forestIris1
##
## Call:
## randomForest(formula = Species ~ Petal.Width + Petal.Length, data = iris, prox = TRUE, ntree = 50)
## Type of random forest: classification
## Number of trees: 50
## No. of variables tried at each split: 1
##
## OOB estimate of error rate: 4.67%
## Confusion matrix:
## setosa versicolor virginica class.error
## setosa 50 0 0 0.00
## versicolor 0 47 3 0.06
## virginica 0 4 46 0.08
forestIris2
##
## Call:
## randomForest(formula = Species ~ Petal.Width + Petal.Length, data = iris, prox = TRUE, ntree = 50)
## Type of random forest: classification
## Number of trees: 50
## No. of variables tried at each split: 1
##
## OOB estimate of error rate: 4%
## Confusion matrix:
## setosa versicolor virginica class.error
## setosa 50 0 0 0.00
## versicolor 0 47 3 0.06
## virginica 0 3 47 0.06
forestIris3
##
## Call:
## randomForest(formula = Species ~ Petal.Width + Petal.Length, data = iris, prox = TRUE, ntree = 50)
## Type of random forest: classification
## Number of trees: 50
## No. of variables tried at each split: 1
##
## OOB estimate of error rate: 5.33%
## Confusion matrix:
## setosa versicolor virginica class.error
## setosa 50 0 0 0.00
## versicolor 0 47 3 0.06
## virginica 0 5 45 0.10
model <- combine(forestIris1, forestIris2, forestIris3)
model
##
## Call:
## randomForest(formula = Species ~ Petal.Width + Petal.Length, data = iris, prox = TRUE, ntree = 50)
## Type of random forest: classification
## Number of trees: 150
## No. of variables tried at each split: 1
Matriz de confusão para o novo modelo:
table(predict(model, iris), iris$Species)
##
## setosa versicolor virginica
## setosa 50 0 0
## versicolor 0 49 1
## virginica 0 1 49
Predizer as classes para novos objetos:
newdata <- data.frame(Sepal.Length <- rnorm(1000, mean(iris$Sepal.Length), sd(iris$Sepal.Length)),
Sepal.Width <- rnorm(1000, mean(iris$Sepal.Width), sd(iris$Sepal.Width)),
Petal.Width <- rnorm(1000, mean(iris$Petal.Width), sd(iris$Petal.Width)),
Petal.Length <- rnorm(1000, mean(iris$Petal.Length), sd(iris$Petal.Length)))
pred <- predict(model, newdata)
Mostrando visualmente que o random forest é capaz de separar conjunto de dados que não são separáveis linearmente:
plot(newdata[, 4], newdata[, 3], pch = 21, xlab = "Petal.Length", ylab = "Petal.Width",
bg = c("red", "blue", "green")[as.numeric(pred)], main = "Novos dados")
