xgboost xgb.dump 树系数 [英] xgboost xgb.dump tree coefficient

问题描述

我这里有一个示例代码.

I have a sample code here.

data(agaricus.train, package='xgboost')
train <- agaricus.train
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
xgb.dump(bst, 'xgb.model.dump', with.stats = TRUE)

建立模型后，我将其打印为

After building the model, I print it out as

booster[0]
0:[f28<-1.00136e-05] yes=1,no=2,missing=1,gain=4000.53,cover=1628.25
    1:[f55<-1.00136e-05] yes=3,no=4,missing=3,gain=1158.21,cover=924.5
        3:leaf=1.71218,cover=812
        4:leaf=-1.70044,cover=112.5
    2:[f108<-1.00136e-05] yes=5,no=6,missing=5,gain=198.174,cover=703.75
        5:leaf=-1.94071,cover=690.5
        6:leaf=1.85965,cover=13.25
booster[1]
0:[f59<-1.00136e-05] yes=1,no=2,missing=1,gain=832.545,cover=788.852
    1:[f28<-1.00136e-05] yes=3,no=4,missing=3,gain=569.725,cover=768.39
        3:leaf=0.784718,cover=458.937
        4:leaf=-0.96853,cover=309.453
    2:leaf=-6.23624,cover=20.4624

我有问题:

1. 我知道梯度提升树的平均值来自这些树，并带有一些加权系数.我怎样才能得到这些系数?

1. I understand that Gradient boost tree averages results from these trees with some weighted coefficients. How can I get those coefs?

只是为了澄清.树预测的值是leaf = x，不是吗?

Just to clarify. The value predicted by the trees are leaf = x, isn't it?

谢谢.

推荐答案

Q1 和 Q2 的组合答案:

Combined answer for Q1 and Q2:

xgboost 的所有树叶分数的系数为 1.只需将所有树叶分数相加即可.设总和为 S.然后对其应用逻辑(2级)函数:Pr(label=1) = 1/(1+exp(-S))

The coefficient for all tree leaf scores for xgboost is 1. Simply sum all the leaf scores. Let the sum be S. Then apply logistic(2-class) function on it: Pr(label=1) = 1/(1+exp(-S))

我已经验证了这一点并在生产系统中使用.

I have verified this and used in production systems.

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