Why is logistic regression called a linear model if it uses a sigmoid?
mediumAnswer
- The decision boundary is linear in the input features because the model is linear in the log-odds: x + b.
- The sigmoid only maps that linear score into [0, 1] for a probability.
- So the model is linear in parameters and features; only the link function is nonlinear.
Check yourself — multiple choice
- It is nonlinear because of the sigmoid
- It is linear in the log-odds
- It is a tree-based model
- It uses only one feature at a time
Logistic regression is linear in log-odds; the decision boundary is a hyperplane.
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