Decision Boundary#
Every classifier partitions its input space into regions, one per class, and the surfaces where the predicted class changes are the decision boundaries. They are not drawn by hand — they are derived from the parameters the model estimates, which is why the same dataset yields very different boundaries under logistic regression, naive Bayes, or nearest neighbours.
This chapter defines the decision boundary formally and derives it for familiar models.