A hypersurface in machine learning that separates different classes in a feature space. It represents the area where the model’s prediction shifts from one class to another, helping the model distinguish between different classes and enable accurate predictions on unseen data.

• Binary classification problems
• Multiple classifications problems
• Regression analysis
• Clustering analysis

For instance, in a two-dimensional feature space, the decision boundary could be a line or curve that separates two classes in a binary classification problem.

• Designing the semantic data model