This is used when your target variable has (e.g., predicting if a user will choose Product A, B, or C).
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 and Multinomial
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
The categories must be nominal (no inherent order). If the categories have a natural ranking (like "Low, Medium, High"), you should use Ordinal Logistic Regression instead. It outputs a vector of probabilities for all
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