Logistic Regression
IMPORTANT: This note is translated by LLM and fixed manually. See Chinese version for a more accurate note 1. Algorithm Overview Logistic regression predicts the probability of an event by mapping the output of linear regression to the probability space: $$ P(y=1|x;\theta)=h_{\theta}(x)=\frac{1}{1+e^{-\theta^Tx}} $$ This formula represents: given model parameters $\theta$ and input feature values $x$, the probability that the classification label $y=1$ is $h_{\theta}(x)$. The pseudocode for a single inference of this model is approximately as follows: ...