当我只使用 mtry
参数作为 tuingrid
时,它起作用了,但是当我添加 ntree
参数时,错误变为 Error in train.default(x,y,weights = w,...):调整参数网格应该有列mtry
。代码如下:
require(RCurl)
require(prettyR)
library(caret)
url <- "https://raw.githubusercontent.com/gastonstat/CreditScoring/master/CleanCreditScoring.csv"
cs_data <- getURL(url)
cs_data <- read.csv(textConnection(cs_data))
classes <- cs_data[, "Status"]
predictors <- cs_data[, -match(c("Status", "Seniority", "Time", "Age", "Expenses",
"Income", "Assets", "Debt", "Amount", "Price", "Finrat", "Savings"), colnames(cs_data))]
train_set <- createDataPartition(classes, p = 0.8, list = FALSE)
set.seed(123)
cs_data_train = cs_data[train_set, ]
cs_data_test = cs_data[-train_set, ]
# Define the tuned parameter
grid <- expand.grid(mtry = seq(4,16,4), ntree = c(700, 1000,2000) )
ctrl <- trainControl(method = "cv", number = 10, summaryFunction = twoClassSummary,classProbs = TRUE)
rf_fit <- train(Status ~ ., data = cs_data_train,
method = "rf",
preProcess = c("center", "scale"),
tuneGrid = grid,
trControl = ctrl,
family= "binomial",
metric= "ROC" #define which metric to optimize metric='RMSE'
)
rf_fit
最佳答案
您必须使用随机森林包创建自定义 RF,然后包含您想要包含的参数。
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, newdata, preProc = NULL, submodels = NULL)
predict(modelFit, newdata)
customRF$prob <- function(modelFit, newdata, preProc = NULL, submodels = NULL)
predict(modelFit, newdata, type = "prob")
customRF$sort <- function(x) x[order(x[,1]),]
customRF$levels <- function(x) x$classes
customRF
然后您可以在训练函数中使用方法作为[customRF]。
关于r - 在 Caret 包中调整随机森林的两个参数,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/38625493/