Seonglae ChoTransform probability to linear form like Linear Regression using Odds ratio
Unlike linear regression, it handles non-linear relationships well while still being mathematically treatable as linear.
It uses a sigmoid function to output values between 0 and 1, maximizing the log-likelihood.
to make convex cost function
Logistic Regression Notion
the Likelihood function is
you can use Newton–Raphson method or Stochastic Gradient Descent and prior one achieves faster convergence or IRLS is much faster
difference between Linear Regression is that usage of sigmoid function or logistic function
a change of one unit of feature changes the odds ratio by a factor
Logistic Multinomial Regression
Multinomial logistic regression
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes.[1] That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc.).
https://en.wikipedia.org/wiki/Multinomial_logistic_regression