Logistic Regression: Binary And Multinomial «Official — 2025»

Use if you are choosing between several distinct labels where one choice doesn't "outrank" another.

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). Use if you are choosing between several distinct

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? It outputs a vector of probabilities for all

Instead of one sigmoid function, it uses the Softmax function . It essentially runs multiple binary regressions comparing each category to a "reference" category.

This is used when your target variable has exactly (e.g., Yes/No, Pass/Fail, Spam/Not Spam).

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.