我尝试为 Ensemble of Ensemble 建模创建一个函数:
library(foreach)
library(randomForest)
set.seed(10)
Y<-round(runif(1000))
x1<-c(1:1000)*runif(1000,min=0,max=2)
x2<-c(1:1000)*runif(1000,min=0,max=2)
x3<-c(1:1000)*runif(1000,min=0,max=2)
all_data<-data.frame(Y,x1,x2,x3)
bagging = function(dataFile, length_divisor = 4, iterations = 100)
{
fit = list()
predictions = foreach(m = 1 : iterations, .combine = cbind) %do%
{
dataFile$Y = as.factor(dataFile$Y)
rf_fit = randomForest(Y ~ ., data = dataFile, ntree = 100)
fit[[m]] = rf_fit
rf_fit$votes[,2]
}
rowMeans(predictions)
return(list(formula = as.formula("Y ~ ."), trees = fit, ntree = 100, class = dataFile$Y, votes = predictions))
}
final_model = bagging(all_data)
predict(final_model, TestData) # It says predict doesn't support final_model object
# Error in UseMethod("predict") : no applicable method for 'predict' applied to an object of class "list"
它说 -
Error in UseMethod("predict") : no applicable method for 'predict' applied to an object of class "list".
我需要上述函数bagging
来返回聚合模型对象,以便我可以预测新数据集。
最佳答案
您的bagging
函数仅返回一个任意列表。 Predict 通过查看第一个参数的类来了解要执行的“正确操作”。我假设您想从列表中存储的 randomForest 对象进行预测?您可以使用 Map()
循环遍历您的列表。例如
Map(function(x) predict(x, TestData), final_model$trees)
(未经测试,因为您似乎没有提供TestData
)
关于r - 如何在bagging中创建模型对象?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/33514130/