1. CS 189 Brian Chu brian.c@berkeley.edu Office Hours: Cory 246, 6-7p Mon. (hackerspace lounge) twitter: @brrrianchu brianchu.com

2. Agenda Email me for slides Questions? Random / HW Why logistic regression Worksheet

3. Questions Any grad students? – Thoughts on final project? Who would be able to make my 12-1pm section? – Lecture / worksheet split section Questions? Concerns? Lecture pace / content / coverage?

4. Features sklearn hog, sklearn tfidf, bag of words, etc.

5. Terminology Shrinkage (regularization) Variable with a hat (ŷ)  estimated/predicted P(Y | X) ∝ P(X |Y) P(Y) posterior ∝ likelihood * prior

6. Why logistic regression Odds measure of relative confidence – P =.9998; 4999:1 – P =.9999; 9999:1 – Doubled confidence!.5001% .5002; 1.0004:1  1.0008:1 – (basically no change in confidence) “relative increase or decrease of a factor by one unit becomes more pronounced as the factors absolute difference increases.”

7. Log-odds (calculations in base 10) (0, 1)  (-∞, ∞) Symmetric:.99 ≈ 2,.01 ≈ -2 X units of log-odds  same Y % change in confidence –0.5  0.91 ≈ 0  1 –.999 .9999 ≈ 3  4 “ Log-odds make it clear that increasing from 99.9% to 99.99% is just as hard as increasing from 50% to 91%” Credit: https://dl.dropboxusercontent.com/u/34547557/log-probability.pdf

8. Logistic Regression w x = lg [ P(Y=1|x) / (1 – P(Y=1|x) ] Intuition: some linear combination of the features tells us the log-odds that Y = 1 Intuition: some linear combination of the features tells us the “confidence” that Y = 1