# Logistic regression (for classification)

- Email: spam or not spam
- Tumor: malignant or benign
- Online Transaction: Fraudlent or not?

Binary classification:

y can be either 0 or 1,

- 0 = Negative class
- 1 = Positive class

Multi-class classification problem when y can have more than 2 distinct values

- Linear regression using a threshold value

- Sigmoid function / Logistic function
- Decision boundary

- The "Logistic regression cost function" based on the Sigmoid function is a non-convex function so Gradient Descent isn't guarnteed to reach global minimum. So intead of that we use some log() function.

Optimization algorithms

- Gradient descent
- Conjugate gradient
- BFGS
- L-BFGS

The other 3 algorthms have the advantage of not needing to pick a alfa (learning pace), and they are often faster than Gradient descent. However they are more complex to implement.