Comments (10)
ps:之所以倾向于构建训练集A,是因为应用场景的特征高维稀疏。训练集A可以降低硬盘存储和训练模型的内存
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你好,
训练数据格式有这么一个约定:如果某一维特征没有出现在样本中,则认为这条样本中这一维特征为缺失(N/A,not available)。
树模型拟合过程中,也会对缺失值进行特殊的处理。
所以目前数据格式中对于特征值为0的情况也得显示的标明。
另外,从训练复杂度来看的话,树模型的训练时间和特征维度成正比,如果特征维度特别高的话,树模型的训练时间会特别大,可以考虑尝试其他模型(LR、FM、FFM等)
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你好,
1、由于一些场景需要,其他模型可能不太适合,需要这个c++的gbdt
2、这两份数据集A和B的信息量是一样,所以期待两分数据抛出来的auc效果是一样的。因为B只是对A的所有缺失值补0而已,信息量没有增加
所以,想问下当前的gbdt对missing value是怎样处理的,以及,有没有推荐的方案供参考
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from gbdt.
比如xgboost的方案是把missing value的样本全分到左孩子节点
from gbdt.
你好,这个实现中,是用一颗“三叉树”来处理missing value情况的:左右子节点+NaN节点。
所以对数据格式有了这样的要求。
如果你期望实现sparse的数据格式,可以简单修改一下数据加载模块:
Line 44 in 5aa5b9f
将初始值从kUnknownValue改为0即可以了。
希望能帮助到你。
多谢
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好的,另外有个疑问哈,如果是三叉树的话,训练集A的auc为什么不是1呢
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特征0 非miss即判断为0类别,miss即判断为1类别
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from gbdt.
okay,了解了,谢谢!那我把所有miss的值用计算机的最小值补全,则miss值会分配到左孩子节点。你觉得这样做会不会有什么问题呢?
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