关于验证数据的 XGBoost 模型性能报告

Question

我想利用 XGBoost 的 early.stop.round 功能进行非过拟合训练。为此，我使用以下代码：

param2 <- list("objective" = "reg:linear",
                     "eval_metric" = "rmse",
                     "max_depth" = 15,
                     "eta" = 0.03,
                     "gamma" = 0,
                     "subsample" = 0.5,
                    "colsample_bytree" = 0.6,
                     "min_child_weight" = 5,
                     "alpha" = 0.15)

  watchlist <- list(train = xgb.DMatrix(data = train_matrix, label = output_train),
                  test = xgb.DMatrix(data = total_matrix[ind, ], label = as.matrix(output_total[ind, ])))

  bst <- xgboost(data=train_matrix, label=output_train, nrounds = 500, watchlist = watchlist,
                        early.stop.round=5,verbose = 2, param=param2, missing = NaN)

因此，根据需要，我为监视列表创建了 train 和 test xgb.DMatrix，并将其传递给 xgboost()。我确保 verbose 在那里打印中间结果。但是 verbose=2 我得到的日志如下：

tree prunning end, 1 roots, 1692 extra nodes, 0 pruned nodes ,max_depth=15
[74]    train-rmse:0.129515
tree prunning end, 1 roots, 1874 extra nodes, 0 pruned nodes ,max_depth=15
[75]    train-rmse:0.128455
tree prunning end, 1 roots, 1826 extra nodes, 0 pruned nodes ,max_depth=15
[76]    train-rmse:0.127804
tree prunning end, 1 roots, 1462 extra nodes, 0 pruned nodes ,max_depth=15
[77]    train-rmse:0.126874
tree prunning end, 1 roots, 1848 extra nodes, 0 pruned nodes ,max_depth=15
[78]    train-rmse:0.125914

while with verbose=1 给我：

[74]    train-rmse:0.129515
[75]    train-rmse:0.128455
[76]    train-rmse:0.127804
[77]    train-rmse:0.126874
[78]    train-rmse:0.125914

但是 none 这让我在测试 DMatrix 的每个步骤都有模型性能。我也试过没有成功：

verbose=T 和 verbose=F.
将 test DMatrix 的名称更改为 validation

我缺少什么以获得所需的输出。

Answer 1

显然，测试数据集性能报告只能使用 xgb.train() 而不是 xgboost() 来完成。相关修改代码（不复制上面的 param 部分）看起来像：

  dtrain <- xgb.DMatrix(data = train_matrix, label = output_train)
  dtest <- xgb.DMatrix(data = total_matrix[ind, ], label = as.matrix(output_total[ind, ]))
  watchlist <- list(train = dtrain, test = dtest)    
  bst <- xgb.train(data= dtrain, nrounds = 500, watchlist = watchlist,
                        prediction = T, early.stop.round=5,verbose = 1, param=param2, missing = NaN)

关于验证数据的 XGBoost 模型性能报告

XGBoost model performance reporting on validation data

r

logging

xgboost