tukivektoreina

Tukivektoreina is a term of Estonian origin that translates to "as support vectors" in English. It is a concept primarily used in the field of machine learning, specifically within the context of Support Vector Machines (SVMs).

In an SVM, the goal is to find an optimal hyperplane that maximally separates data points belonging to different classes. The support vectors are the data points that lie closest to this hyperplane. They are crucial because they uniquely determine the position and orientation of the separating hyperplane. If these support vectors were moved, the hyperplane would also shift. Points that are further away from the hyperplane do not influence its position and are therefore not support vectors.

The term "tukivektoreina" emphasizes the role of these specific data points as the foundational elements or "supports" upon which the SVM model is built. Understanding which data points are support vectors can also offer insights into the decision boundary of the classification model. In practice, algorithms identify these critical points during the training phase of an SVM. The number of support vectors can also impact the efficiency and interpretability of the model.