Hyperplane Machine Learning
Definition: A flat affine subspace of dimension n−1 in an n-dimensional space; in machine learning, the term often refers to a decision surface that separates regions or classes in a high-dimensional feature space. Google’s ML Glossary defines a hyperplane as a boundary that separates a space into two subspaces, and notes that in machine learning it more typically denotes the boundary separating a high-dimensional space [Britannica; Google ML Glossary].
Examples in context: “A hyperplane passing through the non-dominated optimistic values of x is considered the optimistic Pareto front Fopt. Similarly, a pessimistic Pareto front Fpess is constructed by a hyperplane passing through the pessimistic values of x.” [Iqbal et al. 2023]
“SVM is a supervised machine learning algorithm aiming to return an optimal hyperplane that separates two classes of samples with distinct labels and then assigns a label for a new datum determined by which side it lies.” [Xu et al. 2025]
Related terms: decision hyperplane; separating hyperplane; decision boundary (functional interpretation)