Comments (4)
树的训练拟合的是残差,在训练单棵树的时候可以当成回归。
from gbdt.
@liudragonfly
def MSE(values):
"""
均平方误差 mean square error
"""
if len(values) < 2:
return 0
mean = sum(values)/float(len(values))
error = 0.0
for v in values:
error += (mean-v)*(mean-v)
return error
有点不明白,我看均方误差的定义是sum(predict-label)^2,而这里感觉作者代码里面写的MSE是求的value的方差啊……求解答
from gbdt.
树的训练拟合的是残差,在训练单棵树的时候可以当成回归。
在xgboost时划分特征就是根据划分之后loss最小来决定是否划分以及选择最优划分点,请问xgboost和gbdt这两种策略有什么区别吗?为何gbdt不使用类似xgboost那样的策略
from gbdt.
@DeligientSloth 应该是GBDT和xgboost的loss定义不同造成的。xgboost可以看成是优化版本的GBDT。
from gbdt.
Related Issues (13)
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from gbdt.