1. Classification
APAM E4990
Computational Social Science
Jake Hofman
Columbia University
April 26, 2013
Jake Hofman (Columbia University) Classification April 26, 2013 1 / 11

2. Prediction a la Bayes1
• You’re testing for a rare condition:
• 1% of the student population is in this class
• You have a highly sensitive and specific test:
• 99% of students in the class visit compsocialscience.org
• 99% of students who aren’t in the class don’t visit this site
• Given that a student visits the course site, what is probability
the student is in our class?
1
Follows Wiggins, SciAm 2006
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3. Prediction a la Bayes
Students
10,000 ppl
1% In class
100 ppl
99% Visit
99 ppl
1% Don’t visit
1 per
99% Not in class
9900 ppl
1% Visit
99 ppl
99% Don’t visit
9801 ppl
Jake Hofman (Columbia University) Classification April 26, 2013 3 / 11

4. Prediction a la Bayes
Students
10,000 ppl
1% In class
100 ppl
99% Visit
99 ppl
1% Don’t visit
1 per
99% Not in class
9900 ppl
1% Visit
99 ppl
99% Don’t visit
9801 ppl
So given that a student visits the site (198 ppl), there is a 50%
chance the student is in our class (99 ppl)!
Jake Hofman (Columbia University) Classification April 26, 2013 3 / 11

5. Prediction a la Bayes
Students
10,000 ppl
1% In class
100 ppl
99% Visit
99 ppl
1% Don’t visit
1 per
99% Not in class
9900 ppl
1% Visit
99 ppl
99% Don’t visit
9801 ppl
The small error rate on the large population outside of our class
produces many false positives.
Jake Hofman (Columbia University) Classification April 26, 2013 3 / 11

6. Inverting conditional probabilities
Bayes’ Theorem
Equate the far right- and left-hand sides of product rule
p (y|x) p (x) = p (x, y) = p (x|y) p (y)
and divide to get the probability of y given x from the probability
of x given y:
p (y|x) =
p (x|y) p (y)
p (x)
where p (x) = y∈ΩY
p (x|y) p (y) is the normalization constant.
Jake Hofman (Columbia University) Classification April 26, 2013 4 / 11

7. Predictions a la Bayes
Given that a patient tests positive, what is probability the patient
is sick?
p (class|visit) =
99/100
p (visit|class)
1/100
p (class)
p (visit)
99/1002+99/1002=198/1002
=
99
198
=
1
2
where p (visit) = p (visit|class) p (class) + p visit|class p class .
Jake Hofman (Columbia University) Classification April 26, 2013 5 / 11

8. (Super) Naive Bayes
We can use Bayes’ rule to build a one-site student classifier:
p (class|site) =
p (site|class) p (class)
p (site)
where we estimate these probabilities with ratios of counts:
ˆp(site|class) =
# students in class who visit site
# students in class
ˆp(site|class) =
# students not in class who visit site
# students not in class
ˆp(class) =
# students in class
# students
ˆp(class) =
# students not in class
# students
Jake Hofman (Columbia University) Classification April 26, 2013 6 / 11

9. Naive Bayes
Represent each student by a binary vector x where xj = 1 if the
student has visited the j-th site (xj = 0 otherwise).
Modeling each site as an independent Bernoulli random variable,
the probability of visiting a set of sites x given class membership
c = 0, 1:
p (x|c) =
j
θ
xj
jc (1 − θjc)1−xj
where θjc denotes the probability that the j-th site is visited by a
student with class membership c.
Jake Hofman (Columbia University) Classification April 26, 2013 7 / 11

10. Naive Bayes
Using this likelihood in Bayes’ rule and taking a logarithm, we have:
log p (c|x) = log
p (x|c) p (c)
p (x)
=
j
xj log
θjc
1 − θjc
+
j
log(1 − θjc) + log
θc
p (x)
Jake Hofman (Columbia University) Classification April 26, 2013 8 / 11

11. Naive Bayes
We can eliminate p (x) by calculating the log-odds:
log
p (1|x)
p (0|x)
=
j
xj log
θj1(1 − θj0)
θj0(1 − θj1)
wj
+
j
log
1 − θj1
1 − θj0
+ log
θ1
θ0
w0
which gives a linear classifier of the form w · x + w0
Jake Hofman (Columbia University) Classification April 26, 2013 9 / 11

12. Naive Bayes
We train by counting students and sites to estimate θjc and θc:
ˆθjc =
njc
nc
ˆθc =
nc
n
and use these to calculate the weights ˆwj and bias ˆw0:
ˆwj = log
ˆθj1(1 − ˆθj0)
ˆθj0(1 − ˆθj1)
ˆw0 =
j
log
1 − ˆθj1
1 − ˆθj0
+ log
ˆθ1
ˆθ0
.
We we predict by simply adding the weights of the sites that a
student has visited to the bias term.
Jake Hofman (Columbia University) Classification April 26, 2013 10 / 11

13. Naive Bayes
In practice, this works better than one might expect given its
simplicity2
2
http://www.jstor.org/pss/1403452
Jake Hofman (Columbia University) Classification April 26, 2013 11 / 11

14. Naive Bayes
Training is computationally cheap and scalable, and the model is
easy to update given new observations2
2
http://www.springerlink.com/content/wu3g458834583125/
Jake Hofman (Columbia University) Classification April 26, 2013 11 / 11

15. Naive Bayes
Performance varies with document representations and
corresponding likelihood models2
2
http://ceas.cc/2006/15.pdf
Jake Hofman (Columbia University) Classification April 26, 2013 11 / 11

16. Naive Bayes
It’s often important to smooth parameter estimates (e.g., by
adding pseudocounts) to avoid overfitting
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