统计上有效的结合起来推论的结果。
这个原理的一个推论是,一种学习算法永远不会对它训练集的结果进行评估,因为对于一种未知的事例而言,没有证据表明算法具有概括它们的能力。
A corollary of this principle is that a learning algorithm should never be evaluated for its results in the training set because this shows no evidence of an ability to generalize to unseen instances.
如果在另一个服务器上修改相同的属性会造成相同的结果,就可以推论这个操作就是问题的根源。
If changing the same attributes on another server causes the same effect, it is reasonable to deduce that action was the source of the problem.
It's a matter of inference, not just to any old explanation, but inference to the best explanation.
这是逻辑推论的结果,不是随便什么陈旧的解释,而是最佳解释的推论结果
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