[IR] Evaluation

无序检索结果的评价方法：

Precision
 P
 =
tp/(tp
+
fp)

Recall

 



R
     =
tp/(tp
+
fn)


Accuracy   = (tp + tn) / ( tp + fp + fn + tn)

有序检索结果的评价方法：

A precison-recall curve

调式search engine目前只是针对一个Query的表现。

You
 need
 to
 average
 performance 
over
 a
 whole bunch of queries.

其实，就是在遵从precision降低，必然提高recall的原则下，画出趋势图。(也就是插值法 Interpolated
 Precision)

What is the interpolated precision of the system at 25% recall? 

1.0, 0.67, 0.5, 0.4, 0.36, 0.36, 0.36

Mean average precision (MAP)

System: D1, D2, D4, D3

k = 1, R, 1/1

k = 2, NR, n/a

k = 3, NR, n/a

k = 4, R, 2/4

MAP = (1/1+2/4)/2 = 3/4

What is the largest possible mean average precision that this system could have?

If the last two relevant documents are in ranking 21 and 22. 尽量早出现

MAP = (1.0+1.0+0.33+0.36+0.33+0.3+0.33+0.36)/8 = 0.503

What is the smallest possible mean average precision that this system could have?

If the last two relevant documents are in ranking 9999 and 10000. 尽量晚出现

MAP = (1.0+1.0+0.33+0.36+0.33+0.3+0.0007+0.0008)/6 = 0.416

用已有的MAP去估计未来可能的MAP的error是多少？

MAP = (1.0 + 1.0 + 0.33 + 0.36 + 0.33 + 0.3)/6 = 0.555

The error could be 0.555 – (0.503 + 0.416)/2 = 0.095

Kappa Measure

P(A) = Accuracy

P(E) = [ (person1-yes + person2-yes)/(total*2) ]^2 + [ (person1-no + person2-no)/(total*2) ]

Kappa
=
[
P(A)
–
P(E)
]
/
[
1
–
P(E)
]

Kappa
 > 
0.8                // good
 agreement

0.67
 < 
Kappa 
< 
0.8
     // “tentative
 conclusions”
(CarleSa


’96)


相关反馈：有点reinforcement learning的意思。

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