Tuning two parameters for random forest in Caret package

问题:

When i only used mtry parameter as the tuingrid, it worked but when i added ntree parameter the error becomes Error in train.default(x, y, weights = w, ...): The tuning parameter grid should have columns mtry. The code is as below:

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 

回答1:

You have to create a custom RF using the random forest package and then include the param that you want to include.

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 

Then you can use method as [customRF] in the train function.

回答2:

You should change:

grid <- expand.grid(.mtry = seq(4,16,4),. ntree = c(700, 1000,2000) ) 

文章来源: Tuning two parameters for random forest in Caret package