It outputs a vector of probabilities for all classes that sum up to 1.0. The class with the highest probability is the predicted outcome. Key Differences at a Glance Multinomial Outcome Classes Function Example Fraud vs. Not Fraud Red vs. Blue vs. Green Complexity Simple; one set of weights Higher; weights for each class When to Use Which?
This is used when your target variable has (e.g., predicting if a user will choose Product A, B, or C). Logistic Regression: Binary and Multinomial
Use if you are choosing between several distinct labels where one choice doesn't "outrank" another. It outputs a vector of probabilities for all
It uses the Sigmoid function to map any real-valued number into a value between 0 and 1. The Math: It models the "log-odds" of the probability Not Fraud Red vs
Use if you are answering a "True/False" style question.
Instead of one sigmoid function, it uses the Softmax function . It essentially runs multiple binary regressions comparing each category to a "reference" category.
Logistic Regression: Binary vs. Multinomial Logistic regression is a statistical method used to predict the probability of a categorical outcome based on one or more independent variables. Despite the name, it is used for , not regression. 1. Binary Logistic Regression