我试图理解关于在交互深度为1的二进制分类的xgb.dump中发生了什么的直觉。特别是如何在一行中使用相同的分割（f38 <2.5） ）（代码行2和6）
结果输出如下：
xgb.dump(model_2,with.stats=T)
[1] "booster[0]"
[2] "0:[f38<2.5] yes=1,no=2,missing=1,gain=173.793,cover=6317"
[3] "1:leaf=-0.0366182,cover=3279.75"
[4] "2:leaf=-0.0466305,cover=3037.25"
[5] "booster[1]"
[6] "0:[f38<2.5] yes=1,no=2,missing=1,gain=163.887,cover=6314.25"
[7] "1:leaf=-0.035532,cover=3278.65"
[8] "2:leaf=-0.0452568,cover=3035.6"
第一次使用f38和第二次使用f38之间的区别仅仅是剩余拟合吗？起初它对我来说似乎很奇怪，并试图准确理解这里发生了什么！
谢谢！
答案 0 :(得分：1)
首次使用f38和第二次使用f38之间的区别仅仅是剩余拟合吗？
很可能是 - 它在第一轮之后更新渐变并在示例中找到具有分割点的相同特征
这是一个可重复的例子。
请注意我在第二个例子中如何降低学习率并且它找到相同的特征，对于所有三轮再次发现相同的分裂点。在第一个例子中，它在所有3轮中使用不同的特征。
require(xgboost)
data(agaricus.train, package='xgboost')
train <- agaricus.train
dtrain <- xgb.DMatrix(data = train$data, label=train$label)
#high learning rate, finds different first split feature (f55,f28,f66) in each tree
bst <- xgboost(data = train$data, label = train$label, max_depth = 2, eta = 1, nrounds = 3,nthread = 2, objective = "binary:logistic")
xgb.dump(model = bst)
# [1] "booster[0]" "0:[f28<-9.53674e-07] yes=1,no=2,missing=1"
# [3] "1:[f55<-9.53674e-07] yes=3,no=4,missing=3" "3:leaf=1.71218"
# [5] "4:leaf=-1.70044" "2:[f108<-9.53674e-07] yes=5,no=6,missing=5"
# [7] "5:leaf=-1.94071" "6:leaf=1.85965"
# [9] "booster[1]" "0:[f59<-9.53674e-07] yes=1,no=2,missing=1"
# [11] "1:[f28<-9.53674e-07] yes=3,no=4,missing=3" "3:leaf=0.784718"
# [13] "4:leaf=-0.96853" "2:leaf=-6.23624"
# [15] "booster[2]" "0:[f101<-9.53674e-07] yes=1,no=2,missing=1"
# [17] "1:[f66<-9.53674e-07] yes=3,no=4,missing=3" "3:leaf=0.658725"
# [19] "4:leaf=5.77229" "2:[f110<-9.53674e-07] yes=5,no=6,missing=5"
# [21] "5:leaf=-0.791407" "6:leaf=-9.42142"
## changed eta to lower learning rate, finds same feature(f55) in first split of each tree
bst2 <- xgboost(data = train$data, label = train$label, max_depth = 2, eta = .01, nrounds = 3,nthread = 2, objective = "binary:logistic")
xgb.dump(model = bst2)
# [1] "booster[0]" "0:[f28<-9.53674e-07] yes=1,no=2,missing=1"
# [3] "1:[f55<-9.53674e-07] yes=3,no=4,missing=3" "3:leaf=0.0171218"
# [5] "4:leaf=-0.0170044" "2:[f108<-9.53674e-07] yes=5,no=6,missing=5"
# [7] "5:leaf=-0.0194071" "6:leaf=0.0185965"
# [9] "booster[1]" "0:[f28<-9.53674e-07] yes=1,no=2,missing=1"
# [11] "1:[f55<-9.53674e-07] yes=3,no=4,missing=3" "3:leaf=0.016952"
# [13] "4:leaf=-0.0168371" "2:[f108<-9.53674e-07] yes=5,no=6,missing=5"
# [15] "5:leaf=-0.0192151" "6:leaf=0.0184251"
# [17] "booster[2]" "0:[f28<-9.53674e-07] yes=1,no=2,missing=1"
# [19] "1:[f55<-9.53674e-07] yes=3,no=4,missing=3" "3:leaf=0.0167863"
# [21] "4:leaf=-0.0166737" "2:[f108<-9.53674e-07] yes=5,no=6,missing=5"
# [23] "5:leaf=-0.0190286" "6:leaf=0.0182581"