흥미로운 IT

Classification: y = 0 or y = 1

if hθ(x) ≥ 0.5, predict y=1

if hθ(x) < 0.5, predict y=0

⇒ logistic regression: 0 ≤ hθ(x) ≤ 1

Hypothesis Representation

- Sigmoid function (==logistic function)

(cf) hθ(x) = 0.7 ⇒ 70% chance of ~~~

Decision boundary

hθ(x) = g(θ0+θ1x1+θ2x2)  ⇒  predict y=1 if -3+x1+x2 ≥ 0

Cost function

- How to choose parameter θ? 

Simplified cost function and gradient descent

* convert the two lines into one line

Logistic regression cost function

Gradient Descent

 *Looks same as linear regression!

BUT, hθ(x) are different! ==>

Multi-class classification (one-vs-all)

Sigmoid function  VS  softmax classifier

⇒ sigmoid: get percentage on how y might equal to 1 for each class

⇒ softmax: get the distribution of percentage of the classes