# Decision Boundary

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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.

## Table of Contents

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