有關true/false positive/negative定義。
Type I errors (the "false positive"):
the error of rejecting the null hypothesis given that it is actually true; e.g., A court finding a person guilty of a crime that they did not actually commit.
其中false positive又為false detection 或是 false alarm
如果以地址來解釋，地址沒有多餘資訊或不完整，系統卻判別出不完整
Type II errors (the "false negative"):
the error of failing to reject the null hypothesis given that the alternative hypothesis is actually true; e.g., A court finding a person not guilty of a crime that they did actually commit.
地址有多餘的資訊或不完整，但系統判定卻是完整的。
不知道這樣作解釋對不對@@？
Precision and recall, F-measure
precision = tp/(tp+tf)
recall = tp/(tp+fn)
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