In a classification problem, a class boundary (or decision boundary) is the dividing line or surface in the feature space that separates regions belonging to different classes. Intuitively, it’s the threshold where a classifier switches from predicting one class to another. For example, in a 2D feature space, a linear classifier like a logistic regression might have a straight line as the class boundary: points on one side of the line are classified as class A, and points on the other side as class B. More complex models (like nonlinear SVMs or neural networks) can produce nonlinear class boundaries – curves or irregular surfaces that partition the space. These boundaries are crucial for understanding a model’s behavior, as they define the decision regions. In theory, one can talk about the true class boundary (the boundary that perfectly separates the underlying true classes, if the data distribution were fully known). A learned model’s decision boundary is an approximation to this true boundary​.Points near the class boundary are inherently hard to classify (small changes could flip their predicted class), and they often correspond to areas of uncertainty for the model.