r - R 中随机森林的分层抽样

Question

我在randomForest的文档中阅读了以下内容:

strata: A (factor) variable that is used for stratified sampling.

sampsize: Size(s) of sample to draw. For classification, if sampsize is a vector of the length the number of strata, then sampling is stratified by strata, and the elements of sampsize indicate the numbers to be drawn from the strata.

作为引用，该函数的接口(interface)如下:

 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, ...)

我的问题是:究竟如何使用strata和sampsize？这是一个最小的工作示例，我想在其中测试这些参数:

library(randomForest)
iris = read.table("http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data", sep = ",", header = FALSE)
names(iris) = c("sepal.length", "sepal.width", "petal.length", "petal.width", "iris.type")

model = randomForest(iris.type ~ sepal.length + sepal.width, data = iris)

> model
500 samples
  6 predictors
  2 classes: 'Y0', 'Y1' 

No pre-processing
Resampling: Bootstrap (7 reps) 

Summary of sample sizes: 477, 477, 477, 477, 477, 477, ... 

Resampling results across tuning parameters:

  mtry  ROC    Sens  Spec  ROC SD  Sens SD  Spec SD
  2     0.763  1     0     0.156   0        0      
  4     0.782  1     0     0.231   0        0      
  6     0.847  1     0     0.173   0        0      

ROC was used to select the optimal model using  the largest value.
The final value used for the model was mtry = 6.

我之所以使用这些参数，是因为我希望 RF 使用尊重数据中阳性与阴性比例的引导样本。

This other thread ，开始了有关该主题的讨论，但在没有明确如何使用这些参数的情况下就解决了。

Answer 1

这不就是这样吗:

model = randomForest(iris.type ~ sepal.length + sepal.width, 
                     data = iris, 
                     sampsize=c(10,10,10), strata=iris$iris.type)

我确实尝试了 ..., strata=iristype 和 ..., strata='iristype' 但显然代码不是为了解释该值而编写的“数据”论证的环境。我使用结果变量是因为它是该数据集中唯一的因素变量，但我认为它不需要是结果变量。事实上，我认为它绝对不应该是结果变量。该特定模型预计会产生无用的输出，并且仅用于测试语法。