# 1.5 训练/开发/测试集划分（Train/dev/test distributions）

训练、开发、测试集选择设置的一些规则和意见：

* 训练、开发、测试集的设置会对产品带来非常大的影响；
* 在选择**开发集**和**测试集**时要使二者来自同一分布，且从所有数据中随机选取；
* 所选择的开发集和测试集中的数据，要与未来想要或者能够得到的数据类似，即模型数据和未来数据要具有相似性；
* 设置的测试集只要足够大，使其能够在过拟合的系统中给出高方差的结果就可以，也许10000左右的数目足够；
* 设置开发集只要足够使其能够检测不同算法、不同模型之间的优劣差异就可以，百万大数据中1%的大小就足够；

尽量保证dev sets和test sets来源于同一分布且都反映了实际样本的情况。如果dev sets和test sets不来自同一分布，从dev sets上选择的“最佳”模型往往不能够在test sets上表现得很好。好比在dev sets上找到最接近一个靶的靶心的箭，但是test sets提供的靶心却远远偏离dev sets上的靶心，结果肯定无法射中test sets上的靶心位置

![](/files/-Le0cdpL8YXykT2gyFy7)

![](/files/-Le0cdpN7YugNKttHWLb)


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