Question

我正在尝试调整xgboost的Hyperparametes用于分类问题，使用插入库，因为我的数据集中有很多因素，xgboost喜欢数据作为数字，我使用Feature Hashing创建了一个虚拟行，但是当我得到运行插入火车，我收到错误

#Using Feature hashing to convert all the factor variables to dummies
objTrain_hashed = hashed.model.matrix(~., data=train1[,-27], hash.size=2^15, transpose=FALSE)
#created a dense matrix which is normally accepted by xgboost method in R
#Hoping I could pass it caret as well
dmodel  <- xgb.DMatrix(objTrain_hashed[, ], label = train1$Walc)

xgb_grid_1 = expand.grid(
  nrounds = 500, 
  max_depth = c(5, 10, 15), 
  eta = c(0.01, 0.001, 0.0001), 
  gamma = c(1, 2, 3), 
  colsample_bytree = c(0.4, 0.7, 1.0), 
  min_child_weight = c(0.5, 1, 1.5)
)


xgb_trcontrol_1 = trainControl(
  method = "cv",
  number = 3,
  verboseIter = TRUE,
  returnData = FALSE,
  returnResamp = "all",                                                        # save losses across all models
  classProbs = TRUE,                                                           # set to TRUE for AUC to be computed
  summaryFunction = twoClassSummary,
  allowParallel = TRUE
)

xgb_train1 <- train(Walc ~.,dmodel,method = 'xgbTree',trControl = xgb_trcontrol_1,
                    metric = 'accuracy',tunegrid = xgb_grid_1)

我收到以下错误

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

有关如何进行的任何建议吗？

Answer 1

这是因为您在代码的最后部分输入了 dmodel。尝试输入 objTrain_hashed，这是一个矩阵，而不是 xgb.DMatrix

Answer 2

sparse.model.matrix（）而不是hashed.model.matrix怎么样... 它适用于我的电脑...... 并且不要转换为xgb.DMatrix（）把它放在train（）函数中只是sparse.model.matrix（）形式。

像...

model_data <- sparse.model.matrix(Y~., raw_data)

和

xgb_train1 <- train(Y ~.,model_data, <bla bla> ...)

希望它有效......谢谢。

无法将xgb.DMatrix传递给插入符号

2 个答案: