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be5b33prg
homeworks
spam
step2
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2015/12/04 14:22 svobodat [Specifications]
2015/12/01 16:04 xposik [Specifications]
2015/11/25 16:33 xposik [Preparation]
2015/11/25 16:30 xposik [Specifications]
2015/11/25 16:29 xposik [Preparation]
2015/11/25 16:00 xposik [Spam filter - step 2]
2015/11/25 15:59 xposik
2015/11/25 15:58 xposik
2015/11/25 15:57 xposik [Corpus]
2015/11/25 15:54 xposik [Preparation]
2015/10/15 14:59 xposik [Specifications]
2015/10/15 14:55 xposik [Corpus]
2015/10/15 14:50 xposik [Preparation]
2015/10/15 14:49 xposik [Spam filter - step 2]
2015/10/15 13:56 xposik created
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2015/12/04 14:22 svobodat [Specifications]
2015/12/01 16:04 xposik [Specifications]
2015/11/25 16:33 xposik [Preparation]
2015/11/25 16:30 xposik [Specifications]
2015/11/25 16:29 xposik [Preparation]
2015/11/25 16:00 xposik [Spam filter - step 2]
2015/11/25 15:59 xposik
2015/11/25 15:58 xposik
2015/11/25 15:57 xposik [Corpus]
2015/11/25 15:54 xposik [Preparation]
2015/10/15 14:59 xposik [Specifications]
2015/10/15 14:55 xposik [Corpus]
2015/10/15 14:50 xposik [Preparation]
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2015/10/15 13:56 xposik created
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courses:be5b33prg:homeworks:spam:step2 [2015/11/25 16:29]
xposik
[Preparation]
courses:be5b33prg:homeworks:spam:step2 [2015/11/25 16:30]
xposik
[Specifications]
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* Function ''compute_confusion_matrix()'' represents the basis for evaluation of the filter performance.
* Function ''compute_confusion_matrix()'' represents the basis for evaluation of the filter performance.
* The function can be used in the following way:<code python>
* The function can be used in the following way:<code python>
-
>>> cm1 = compute_confusion_matrix({}, {})
+
>>> cm1 = compute_confusion_matrix({}, {})
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>>> print(cm1)
+
>>> print(cm1)
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ConfMat(tp=0, tn=0, fp=0, fn=0)
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ConfMat(tp=0, tn=0, fp=0, fn=0)
</code>or<code python>
</code>or<code python>
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>>> truth_dict = {'em1': 'SPAM', 'em2': 'SPAM', 'em3': 'OK', 'em4':'OK'}
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>>> truth_dict = {'em1': 'SPAM', 'em2': 'SPAM', 'em3': 'OK', 'em4':'OK'}
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>>> pred_dict = {'em1': 'SPAM', 'em2': 'OK', 'em3': 'OK', 'em4':'SPAM'}
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>>> pred_dict = {'em1': 'SPAM', 'em2': 'OK', 'em3': 'OK', 'em4':'SPAM'}
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>>> cm2 = compute_confusion_matrix(truth_dict, pred_dict, pos_tag='SPAM', neg_tag='OK')
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>>> cm2 = compute_confusion_matrix(truth_dict, pred_dict, pos_tag='SPAM', neg_tag='OK')
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>>> print(cm2)
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>>> print(cm2)
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ConfMat(tp=1, tn=1, fp=1, fn=1)
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ConfMat(tp=1, tn=1, fp=1, fn=1)
</code>
</code>
courses/be5b33prg/homeworks/spam/step2.txt
· Last modified: 2015/12/04 14:22 by
svobodat