下面xgboost模型树图中'leaf'的值是什么意思?

问题描述

鉴于上述(树枝)条件存在，我猜测这是条件概率.不过我不是很清楚.

I am guessing that it is conditional probability given that the above (tree branch) condition exists. However, I am not clear on it.

如果您想了解有关所用数据的更多信息或我们如何获得此图表，请访问:http://machinelearningmastery.com/visualize-gradient-boosting-decision-trees-xgboost-python/

If you want to read more about the data used or how do we get this diagram then go to : http://machinelearningmastery.com/visualize-gradient-boosting-decision-trees-xgboost-python/

推荐答案

属性 leaf 是预测值.换句话说，如果树模型的评估在那个终端节点(又名叶节点)结束，那么这就是返回的值.

Attribute leaf is the predicted value. In other words, if the evaluation of a tree model ends at that terminal node (aka leaf node), then this is the value that is returned.

在伪代码中(树模型最左边的分支):

In pseudocode (the left-most branch of your tree model):

if(f1 < 127.5){
  if(f7 < 28.5){
    if(f5 < 45.4){
      return 0.167528f;
    } else {
      return 0.05f;
    }
  }
}

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