Question

我使用以下代码调整从here改编的Xgboost实现的参数：

searchGridSubCol <- expand.grid(subsample = c(0.5, 0.75, 1), 
                                colsample_bytree = c(0.6, 0.8, 1))
ntrees <- 100

#Build a xgb.DMatrix object
#DMMatrixTrain <- xgb.DMatrix(data = yourMatrix, label = yourTarget)

rmseErrorsHyperparameters <- apply(searchGridSubCol, 1, function(parameterList){

  #Extract Parameters to test
  currentSubsampleRate <- parameterList[["subsample"]]
  currentColsampleRate <- parameterList[["colsample_bytree"]]

  xgboostModelCV <- xgb.cv(data = as.matrix(train), nrounds = ntrees, nfold = 5, showsd = TRUE, label = traintarget,
                           metrics = "rmse", verbose = TRUE, "eval_metric" = "rmse",
                           "objective" = "reg:linear", "max.depth" = 15, "eta" = 2/ntrees,                               
                           "subsample" = currentSubsampleRate, "colsample_bytree" = currentColsampleRate)

  xvalidationScores <- as.data.frame(xgboostModelCV)
  #Save rmse of the last iteration
  rmse <- tail(xvalidationScores$test.rmse.mean, 1)

  return(c(rmse, currentSubsampleRate, currentColsampleRate))

})

但是，当存储XGBoostModelCV时，我收到以下错误：

 Error in as.data.frame.default(xgboostModelCV) : 
  cannot coerce class ""xgb.cv.synchronous"" to a data.frame

有人可以向我解释导致此错误的原因以及我该如何解决？

Answer 1

上述内容应由以下方式确定：

xvalidationScores <- xgboostModelCV
#Save rmse of the last iteration
rmse <- tail(xvalidationScores$evaluation_log$test_rmse_mean, 1)

自定义Xgboost Hyperparameter调整

1 个答案: