Logistic Regression: Binary And Multinomial Direct

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

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? Logistic Regression: Binary and Multinomial

Use if you are answering a "True/False" style question. It uses the Sigmoid function to map any