Question

customRF <- list(type = "Classification", library = "randomForest", loop = NULL)
customRF$parameters <- data.frame(parameter = c("mtry", "ntree"), class = 
                       rep("numeric", 2), label = c("mtry", "ntree"))
customRF$grid <- function(x, y, len = NULL, search = "grid") {}
customRF$fit <- function(x, y, wts, param, lev, last, weights, classProbs, ...) 
                {randomForest(x,y, mtry = param$mtry, ntree=param$ntree, ...)
                }
customRF$predict <- function(modelFit, test, preProc = NULL, submodels = NULL){
                     predict(modelFit, test)}
customRF$prob <- function(modelFit, test, preProc = NULL, submodels = NULL){
                 predict(modelFit, test, type = "prob")}
customRF$sort <- function(x){x[order(x[,1]),]}
customRF$levels <- function(x){ x$classes}


control <- trainControl(method="repeatedcv", number=10, 
           repeats=3,search="grid")
tunegrid <- expand.grid(.mtry=c(1:13), .ntree=c(500, 700, 900, 1000))
custom <- train(disease~., data=cardi, method=customRF, metric=metric, 
              tuneGrid=tunegrid, trControl=control, na.action = na.exclude)
     summary(custom)
     plot(custom)

我在处理此代码时遇到了很多麻烦，因此它绘制了.mtry = c（1:13）并为四个.ntree选项中的每一个都有单独的一行。

control <- trainControl(method="repeatedcv", number=10, repeats=3, 
            search="grid")
tunegrid <- expand.grid(.mtry=c(1:13))
rf_gridsearch <- train(disease~., data=cardi, method="rf", metric=metric, 
tuneGrid=tunegrid, trControl=control, na.action=na.exclude)
plot(rf_gridsearch)

上面的代码将打印以下图像，该图像几乎在那里，但是我想对每个ntree选项重复该过程，以便最佳地优化randomForest的参数。任何帮助将不胜感激！

原始代码为：https://machinelearningmastery.com/tune-machine-learning-algorithms-in-r/#comment-457883

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0 个答案: