Review
B.Ramamurthy
CSE651, B. Ramamurthy
1
7/11/2014
Classifiers
 Generative vs. discriminative classifiers
 Probabilistic models
 Generative: based on (affirmative) prior knowledge
(like Naïve Bayes prior)
 Discriminative: based on discriminating knowledge:
“this and not this” happening: logistic regression
 We covered Bayes for test 2, we will cover logistic
regression for final exam
CSE651, B. Ramamurthy
2
7/11/2014
Odds ratio and logistic regression
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From probability to odds ratio:
Lets say the probability of success of an event is 0.8; failure is 1 – 0.8 = 0.2
Then the odds of success are : 0.8/0.2 = 4
That is odds of success is 4: 1
Transformation from probability to odds is a monotonic function. As the
probability increases so does the odds.
Examples: 0.9  odds 9: 1 log of odds  2.19
0.999 odds is 999: 1 log of odds 6.9
0.9999 9999:1 log of odds  9.2
0.5 odds 1:1 log of odds  0
0.2  odds 0.25:11:4 log of odds  --1.38
Log of odds is also monotonic function: it is negative till p =0.5
Very often this is what is used in many cases where plausibility of an event
needs to proven.
LR is discriminative since is uses positive + negative occurrence probs in its
calculation.
CSE651, B. Ramamurthy
3
7/11/2014
Final exam
 Simple logistic regression problem: 10 points
 Simples Processing problem: 10 points
 For Android: no coding but questions about the
components : 30 points
 For JS: coding: simple examples: html, css, and js: 30
points
 Cloud computing: Google app engine: 10 points; aws
amazon cloud: 10 points; general concepts such as
PaaS, Saas, Iaas are also included.
CSE651, B. Ramamurthy
4
7/11/2014