Binary And Multinomial: Logistic Regression:

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 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 Logistic Regression: Binary and Multinomial

This is used when your target variable has exactly (e.g., Yes/No, Pass/Fail, Spam/Not Spam). It outputs a vector of probabilities for all