This is used when your target variable has exactly (e.g., Yes/No, Pass/Fail, Spam/Not Spam).
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). This is used when your target variable has exactly (e
Use if you are choosing between several distinct labels where one choice doesn't "outrank" another. Use if you are choosing between several distinct
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 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?