Logistic Regression - Hypothesis Representation

Logistic Regression - Hypothesis Representation

Linear regression: \(h_\theta(x) = \theta^{T}x \)

Want \(0 <= h_\theta(x) <= 1\)
Logistic regression: \(h_\theta(x) = g(\theta^{T}x) \) where \(g(z) = \frac{1}{1 + e^{-z}}\) (sigmoid/logistic function)

Resources:
- https://www.coursera.org/learn/machine-learning/supplement/AqSH6/hypothesis-representation