Logistic Regression: Binary And Multinomial Apr 2026

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.

Use if you are answering a "True/False" style question. Logistic Regression: Binary and Multinomial

This is used when your target variable has (e.g., predicting if a user will choose Product A, B, or C). The categories must be nominal (no inherent order)

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? one set of weights Higher