随机森林
2016-09-11 本文已影响956人
一刀YiDao
1、让两个以及两个以上组合树变成一颗树：combine()
combine(...)
...：每个随机森林对象
data(iris)
rf1 <- randomForest(Species ~ ., iris, ntree=50, norm.votes=FALSE)
rf2 <- randomForest(Species ~ ., iris, ntree=50, norm.votes=FALSE)
rf3 <- randomForest(Species ~ ., iris, ntree=50, norm.votes=FALSE)
rf.all <- combine(rf1, rf2, rf3)
print(rf.all)
2、从森林中提取一颗树：getTree()
getTree(rfobj, k=1, labelVar=FALSE)
rfobj：随机森林对象
k：提取树的个数
labelVar：FALSE or TRUE，更好的标签被用于分裂变量和预测的类别
- 对于数值预测，数据与变量的值小于或等于分裂点去到左子节点。
- 对于分类的预测，分裂点代表一个整数，依据其二进制扩展可以判断该类别是去左子节点还是去右子节点。
例如，如果一个预测四大类，分裂点为13。13的二进制扩展是（1，0，1，1）（因为
13 = 1(2^0)+0(21)+1*(22)+1*(2^3)），所以类别1、3或4被预测到左子节点，其余的到右子节点。
一个矩阵（或数据框，如果labelvar = true）六列六行，总数等于树中的节点数。六列是：
左子女：左子节点所在的行；如果节点为终端的话，则为0
右子女：右子节点所在的行；如果节点为终端的话，则为0
分裂的变量：被用来分割的节点；如果节点是终端，则为0
分裂点：最好的分裂点
状态：节点终端（- 1）或不（1）
预测：节点的预测；如果节点不是终端，则为0
data(iris)
## Look at the third trees in the forest.
getTree(randomForest(iris[,-5], iris[,5], ntree=10), 3, labelVar=TRUE)
left daughter right daughter split var split point status prediction
1 2 3 Petal.Width 0.80 1 <NA>
2 0 0 <NA> 0.00 -1 setosa
3 4 5 Petal.Width 1.55 1 <NA>
4 6 7 Petal.Length 5.00 1 <NA>
5 8 9 Petal.Width 1.75 1 <NA>
6 0 0 <NA> 0.00 -1 versicolor
7 0 0 <NA> 0.00 -1 virginica
8 10 11 Petal.Length 5.40 1 <NA>
9 12 13 Sepal.Width 3.15 1 <NA>
10 14 15 Sepal.Width 2.75 1 <NA>
11 0 0 <NA> 0.00 -1 virginica
12 0 0 <NA> 0.00 -1 virginica
13 16 17 Sepal.Length 6.35 1 <NA>
14 0 0 <NA> 0.00 -1 virginica
15 0 0 <NA> 0.00 -1 versicolor
16 0 0 <NA> 0.00 -1 versicolor
17 0 0 <NA> 0.00 -1 virginica
3、增加树的数量：grow()
grow(x, how.many, ...)
x：随机森林对象
how.many：增加树的数量
data(iris)
iris.rf <- randomForest(Species ~ ., iris, ntree=50, norm.votes=FALSE)
iris.rf <- grow(iris.rf, 50)
print(iris.rf)
Call:
randomForest(formula = Species ~ ., data = iris, ntree = 50, norm.votes = FALSE)
Type of random forest: classification
Number of trees: 100
No. of variables tried at each split: 2
4、提取特征的重要性：importance()
importance(x, type=NULL, class=NULL, scale=TRUE, ...)
x：随机森林对象
type ：1或者2,重要性度量类型，1代表均值降序精度，2代表均值降序节点纯度
class：类别度量
set.seed(4543)
data(mtcars)
mtcars.rf <- randomForest(mpg ~ ., data=mtcars, ntree=1000,
keep.forest=FALSE, importance=TRUE)
importance(mtcars.rf)
importance(mtcars.rf, type=1)
> importance(mtcars.rf)
%IncMSE IncNodePurity
cyl 16.208012 175.57529
disp 17.799412 234.65265
hp 18.129872 190.21859
drat 5.958783 61.17834
wt 19.981446 268.18939
qsec 5.674305 31.13353
vs 6.146763 32.52011
am 5.018332 17.63306
gear 3.301661 16.44901
carb 9.244852 31.14588
> importance(mtcars.rf, type=1)
%IncMSE
cyl 16.208012
disp 17.799412
hp 18.129872
drat 5.958783
wt 19.981446
qsec 5.674305
vs 6.146763
am 5.018332
gear 3.301661
carb 9.244852
5、画图：plot()
plot(x, sort=TRUE, ...)
set.seed(1)
data(iris)
iris.rf <- randomForest(Species ~ ., iris, keep.forest=FALSE)
plot(margin(iris.rf))
randomForest.png
6、分类图：MDSplot()
MDSplot(rf, fac, k=2, palette=NULL, pch=20, ...)
rf：随机森林对象
fac：训练随机森林的因子
k：维度数
palette：颜色
set.seed(1)
data(iris)
iris.rf <- randomForest(Species ~ ., iris, proximity=TRUE,
keep.forest=FALSE)
MDSplot(iris.rf, iris$Species)
## Using different symbols for the classes:
MDSplot(iris.rf, iris$Species, palette=rep(1, 3), pch=as.numeric(iris$Species))
MDSplot.png
7、填补缺失值的中位数：na.roughfix()
na.roughfix(object, ...)
数值变量：缺失值使用中位数替代
因子自变量：缺失值使用最常出现的替代
data(iris)
iris.na <- iris
set.seed(111)
iris.na
## artificially drop some data values.
for (i in 1:4) iris.na[sample(150, sample(20)), i] <- NA
iris.roughfix <- na.roughfix(iris.na)
iris.narf <- randomForest(Species ~ ., iris.na, na.action=na.roughfix)
print(iris.narf)
Call:
randomForest(formula = Species ~ ., data = iris.na, na.action = na.roughfix)
Type of random forest: classification
Number of trees: 500
No. of variables tried at each split: 2
OOB estimate of error rate: 4.67%
Confusion matrix:
setosa versicolor virginica class.error
setosa 50 0 0 0.00
versicolor 0 46 4 0.08
virginica 0 3 47 0.06
8、异常值（离群点）：outlier()
outlier(x, ...)
set.seed(1)
iris.rf <- randomForest(iris[,-5], iris[,5], proximity=TRUE)
plot(outlier(iris.rf), type="h",
col=c("red", "green", "blue")[as.numeric(iris$Species)])
outlier.png
9、用图形化描述变量的边际效应（分类或回归）：partialPlot()
partialPlot(x, pred.data, x.var, which.class,
w, plot = TRUE, add = FALSE,
n.pt = min(length(unique(pred.data[, xname])), 51),
rug = TRUE, xlab=deparse(substitute(x.var)), ylab="",
main=paste("Partial Dependence on", deparse(substitute(x.var))),
...)
x：随机森林对象
pred.data：预测数据
x.var：变量名称
which.class：分类数据
w：权重
data(iris)
set.seed(543)
iris.rf <- randomForest(Species~., iris)
partialPlot(iris.rf, iris, Petal.Width, "versicolor")
## Looping over variables ranked by importance:
data(airquality)
airquality <- na.omit(airquality)
set.seed(131)
ozone.rf <- randomForest(Ozone ~ ., airquality, importance=TRUE)
imp <- importance(ozone.rf)
impvar <- rownames(imp)[order(imp[, 1], decreasing=TRUE)]
op <- par(mfrow=c(2, 3))
for (i in seq_along(impvar)) {
partialPlot(ozone.rf, airquality, impvar[i], xlab=impvar[i],
main=paste("Partial Dependence on", impvar[i]),
ylim=c(30, 70),col='blue')
}
par(op)
partialPlot.png
10、画均值方差：Plot()
plot(x, type="l", main=deparse(substitute(x)), ...)
data(mtcars)
plot(randomForest(mpg ~ ., mtcars, keep.forest=FALSE, ntree=100), log="y",col='red')
MSE.png
11、预测测试数据：predict()
predict(object, newdata, type="response",
norm.votes=TRUE, predict.all=FALSE, proximity=FALSE, nodes=FALSE,
cutoff, ...)
object：随机森林对象
newdata：预测新的数据
type：使用概率矩阵或者还是使用计数投票矩阵
norm.votes：计数投票矩阵标准化
predict.all：是否使用所有的树进行预测
nodes：最终端的节点
cutoff：
data(iris)
set.seed(111)
ind <- sample(2, nrow(iris), replace = TRUE, prob=c(0.8, 0.2))
iris.rf <- randomForest(Species ~ ., data=iris[ind == 1,])
iris.pred <- predict(iris.rf, iris[ind == 2,])
table(observed = iris[ind==2, "Species"], predicted = iris.pred)
## Get prediction for all trees.
predict(iris.rf, iris[ind == 2,], predict.all=TRUE)
## Proximities.
predict(iris.rf, iris[ind == 2,], proximity=TRUE)
## Nodes matrix.
str(attr(predict(iris.rf, iris[ind == 2,], nodes=TRUE), "nodes"))
12、随机森林模型：randomForest()
## S3 method for class ’formula’
randomForest(formula, data=NULL, ..., subset, na.action=na.fail)
## Default S3 method:
randomForest(x, y=NULL, xtest=NULL, ytest=NULL, ntree=500,
mtry=if (!is.null(y) && !is.factor(y))
max(floor(ncol(x)/3), 1) else floor(sqrt(ncol(x))),
replace=TRUE, classwt=NULL, cutoff, strata,
sampsize = if (replace) nrow(x) else ceiling(.632*nrow(x)),
nodesize = if (!is.null(y) && !is.factor(y)) 5 else 1,
maxnodes = NULL,
importance=FALSE, localImp=FALSE, nPerm=1,
proximity, oob.prox=proximity,
norm.votes=TRUE, do.trace=FALSE,
keep.forest=!is.null(y) && is.null(xtest), corr.bias=FALSE,
keep.inbag=FALSE, ...)
## S3 method for class ’randomForest’
print(x, ...)
分类:
> data(iris)
> set.seed(71)
> iris.rf <- randomForest(Species ~ ., data=iris, importance=TRUE,
+ proximity=TRUE)
> print(iris.rf)
Call:
randomForest(formula = Species ~ ., data = iris, importance = TRUE, proximity = TRUE)
Type of random forest: classification
Number of trees: 500
No. of variables tried at each split: 2
OOB estimate of error rate: 5.33%
Confusion matrix:
setosa versicolor virginica class.error
setosa 50 0 0 0.00
versicolor 0 46 4 0.08
virginica 0 4 46 0.08
> round(importance(iris.rf), 2)
setosa versicolor virginica MeanDecreaseAccuracy
Sepal.Length 6.04 7.85 7.93 11.51
Sepal.Width 4.40 1.03 5.44 5.40
Petal.Length 21.76 31.33 29.64 32.94
Petal.Width 22.84 32.67 31.68 34.50
MeanDecreaseGini
Sepal.Length 8.77
Sepal.Width 2.19
Petal.Length 42.54
Petal.Width 45.77
> 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$Species)],
+ main="Iris Data: Predictors and MDS of Proximity Based on RandomForest")
> par(op)
rf.png
无监督案例:
> set.seed(17)
> iris.urf <- randomForest(iris[, -5])
> MDSplot(iris.urf, iris$Species)
> ## stratified sampling: draw 20, 30, and 20 of the species to grow each tree.
> (iris.rf2 <- randomForest(iris[1:4], iris$Species,
+ sampsize=c(20, 30, 20)))
Call:
randomForest(x = iris[1:4], y = iris$Species, sampsize = c(20, 30, 20))
Type of random forest: classification
Number of trees: 500
No. of variables tried at each split: 2
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
rf2.png
回归:
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$proximity)
13、交叉验证进行特征选择：rfcv()
rfcv(trainx, trainy, cv.fold=5, scale="log", step=0.5,
mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE, ...)
trainx：自变量
trainy：因变量
cv.fold：几折交叉验证
set.seed(647)
myiris <- cbind(iris[1:4], matrix(runif(96 * nrow(iris)), nrow(iris), 96))
result <- rfcv(myiris, iris$Species, cv.fold=3)
with(result, plot(n.var, error.cv, log="x", type="o", lwd=2))
## The following can take a while to run, so if you really want to try
## it, copy and paste the code into R.
## Not run:
result <- replicate(5, rfcv(myiris, iris$Species), simplify=FALSE)
error.cv <- sapply(result, "[[", "error.cv")
matplot(result[[1]]$n.var, cbind(rowMeans(error.cv), error.cv), type="l",
lwd=c(2, rep(1, ncol(error.cv))), col=1, lty=1, log="x",
xlab="Number of variables", ylab="CV Error")
## End(Not run)
rfcv.png
14、随机森林填补缺失值：rfImpute ()
## Default S3 method:
rfImpute(x, y, iter=5, ntree=300, ...)
## S3 method for class ’formula’
rfImpute(x, data, ..., subset)
x：自变量
y：因变量
data：数据框
data(iris)
iris.na <- iris
set.seed(111)
## artificially drop some data values.
for (i in 1:4) iris.na[sample(150, sample(20)), i] <- NA
set.seed(222)
iris.imputed <- rfImpute(Species ~ ., iris.na)
set.seed(333)
iris.rf <- randomForest(Species ~ ., iris.imputed)
print(iris.rf)
15、最优抽样：rfImpute()
tuneRF(x, y, mtryStart, ntreeTry=50, stepFactor=2, improve=0.05,
trace=TRUE, plot=TRUE, doBest=FALSE, ...)
data(fgl, package="MASS")
fgl.res <- tuneRF(fgl[,-10], fgl[,10], stepFactor=1.5)
16、变量的重要性：varImpPlot()
arImpPlot(x, sort=TRUE, n.var=min(30, nrow(x$importance)),
type=NULL, class=NULL, scale=TRUE,
main=deparse(substitute(x)), ...)
set.seed(4543)
data(mtcars)
mtcars.rf <- randomForest(mpg ~ ., data=mtcars, ntree=1000, keep.forest=FALSE,
importance=TRUE)
varImpPlot(mtcars.rf)
varImpPlot.png
