"measure"参数在xgb.plot.importance中是什么意思
What does "measure" parameter mean in xgb.plot.importance
我正在尝试使用命令
在 R 中构建 xgboost 模型
bstSparsedel <- xgboost(data = data, label = data[,1],max_depth = 6, eta = .3, nthread = 4, nrounds = nrow(train),verbose = 1,early_stopping_rounds = 1)
然后尝试绘制变量重要性图。其中有一个名为 measure 的参数，它具有三种类型，即覆盖、增益、频率。不同的参数是什么意思？随着每一次测量的情节完全改变。
我试着找了很多地方。如果您能分享 link 会很有帮助。
来自 Understand Your dataset with XGBoost...:
Gain is the improvement in accuracy brought by a feature to the branches it is on. The idea is that before adding a new split on a feature X to the branch there was some wrongly classified elements, after adding the split on this feature, there are two new branches, and each of these branch is more accurate (one branch saying if your observation is on this branch then it should be classified as 1, and the other branch saying the exact opposite).
Cover measures the relative quantity of observations concerned by a feature.
Frequency is a simpler way to measure the Gain. It just counts the number of times a feature is used in all generated trees. You should not use it (unless you know why you want to use it).
我正在尝试使用命令
在 R 中构建 xgboost 模型bstSparsedel <- xgboost(data = data, label = data[,1],max_depth = 6, eta = .3, nthread = 4, nrounds = nrow(train),verbose = 1,early_stopping_rounds = 1)
然后尝试绘制变量重要性图。其中有一个名为 measure 的参数，它具有三种类型，即覆盖、增益、频率。不同的参数是什么意思？随着每一次测量的情节完全改变。
我试着找了很多地方。如果您能分享 link 会很有帮助。
来自 Understand Your dataset with XGBoost...:
Gain is the improvement in accuracy brought by a feature to the branches it is on. The idea is that before adding a new split on a feature X to the branch there was some wrongly classified elements, after adding the split on this feature, there are two new branches, and each of these branch is more accurate (one branch saying if your observation is on this branch then it should be classified as 1, and the other branch saying the exact opposite).
Cover measures the relative quantity of observations concerned by a feature.
Frequency is a simpler way to measure the Gain. It just counts the number of times a feature is used in all generated trees. You should not use it (unless you know why you want to use it).