lecture14-SVMs-handout-6-per

1532 howmanycategories afewwellseparatedones

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Unformatted text preview: ther to endorse concepts of a national PRV (pseudorabies virus) control and eradication program, the NPPC said. A large trade show, in conjunction with the congress, will feature the latest in technology in all areas of the industry, the NPPC added. Reuter &#3;</BODY></TEXT></REUTERS> 23
   Accuracy:
(1
‐
error
rate)
Frac)on
of
 docs
classified
correctly:
 24
 4 Sec. 15.2.4 Introduc)on to Informa)on Retrieval Micro‐
vs.
Macro‐Averaging
 Micro‐
vs.
Macro‐Averaging:
Example
   If
we
have
more
than
one
class,
how
do
we
combine
 mul)ple
performance
measures
into
one
quan)ty?
   Macroaveraging:
Compute
performance
for
each
 class,
then
average.
   Microaveraging:
Collect
decisions
for
all
classes,
 compute
con)ngency
table,
evaluate.
 Class 1 Truth: yes Truth: no Truth: yes Truth: no 10 Classifi er: yes 90 10 Classifier: yes 100 20 970 Classifi er: no 10 890 Classifier: no 20 1860   Sec. 15.2.4 Micro Ave. Table Truth: yes Classifi 10 er: no   25
 Class 2 Truth: no Classifi 10 er: yes   Introduc)on to Informa)on Retrieval Sec. 15.2.4 Introduc)on to Informa)on Retrieval Macroaveraged precision: (0.5 + 0.9)/2 = 0.7 Microaveraged precision: 100/120 = .83 Microaveraged score is dominated by score on common classes 26
 Introduc)on to Informa)on Retrieval Precision‐recall
for
category:
Crude
 Recall LSVM Decision Tree Naïve Bayes Rocchio Precision 27
 Sec. 15.2.4 Introduc)on to Informa)on Retrieval Dumais (1998) 28
 Sec. 15.2.4 Introduc)on to Informa)on Retrieval Good
prac)ce
department:
 Confusion
matrix
 Precision‐recall
for
category:
Ship
 This (i, j) entry means 53 of the docs actually in class i were put in class j by the classifier. Recall Actual Class Class assigned by classifier LSVM Decision Tree Naïve Bayes Rocchio Precision Dumais (1...
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This document was uploaded on 02/26/2014.

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