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- Examples
- ## Classification:
- ##data(iris)
- set.seed(71)
- iris.rf <- randomForest(Species ~ ., data=iris, importance=TRUE,
- proximity=TRUE)
- print(iris.rf)
- ## Look at variable importance:
- round(importance(iris.rf), 2)
- ## Do MDS on 1 - proximity:
- iris.mds <- cmdscale(1 - iris.rf$proximity, eig=TRUE)
- op <- par(pty="s")
- pairs(cbind(iris[,1:4], iris.mds$points), cex=0.6, gap=0,
- col=c("red", "green", "blue")[as.numeric(iris$Species)],
- main="Iris Data: Predictors and MDS of Proximity Based on RandomForest")
- par(op)
- print(iris.mds$GOF)
- ## The `unsupervised' case:
- set.seed(17)
- iris.urf <- randomForest(iris[, -5])
- MDSplot(iris.urf, iris$Species)
- ## stratified sampling: draw 20, 30, and 20 of the species to grow each tree.
- (iris.rf2 <- randomForest(iris[1:4], iris$Species,
- sampsize=c(20, 30, 20)))
- ## Regression:
- ## data(airquality)
- set.seed(131)
- ozone.rf <- randomForest(Ozone ~ ., data=airquality, mtry=3,
- importance=TRUE, na.action=na.omit)
- print(ozone.rf)
- ## Show "importance" of variables: higher value mean more important:
- round(importance(ozone.rf), 2)
- ## "x" can be a matrix instead of a data frame:
- set.seed(17)
- x <- matrix(runif(5e2), 100)
- y <- gl(2, 50)
- (myrf <- randomForest(x, y))
- (predict(myrf, x))
- ## "complicated" formula:
- (swiss.rf <- randomForest(sqrt(Fertility) ~ . - Catholic + I(Catholic < 50),
- data=swiss))
- (predict(swiss.rf, swiss))
- ## Test use of 32-level factor as a predictor:
- set.seed(1)
- x <- data.frame(x1=gl(53, 10), x2=runif(530), y=rnorm(530))
- (rf1 <- randomForest(x[-3], x[[3]], ntree=10))
- ## Grow no more than 4 nodes per tree:
- (treesize(randomForest(Species ~ ., data=iris, maxnodes=4, ntree=30)))
- ## test proximity in regression
- iris.rrf <- randomForest(iris[-1], iris[[1]], ntree=101, proximity=TRUE, oob.prox=FALSE)
- str(iris.rrf$proximity)
复制代码
https://cran.r-project.org/web/packages/randomForest/randomForest.pdf
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