Moniluokkainen

Moniluokkainen is a Finnish adjective meaning “consisting of several classes” or “relating to multiple categories.” It is used to describe systems, datasets, or problems that involve more than one category. The term is common in fields such as machine learning, information science, linguistics, and education, where it helps distinguish problems spanning three or more labels from single-class or binary cases.

In machine learning, moniluokkainen luokittelu refers to multi-class classification, where each instance is assigned to one of several possible classes, as opposed to kaksiluokkainen (binary) classification. Typical tasks include digit recognition with ten classes or object categorization with multiple labels. Modeling approaches include algorithms capable of handling multiple classes, such as logistic regression, support vector machines, decision trees, random forests, and neural networks. Strategies to manage multiple classes include one-vs-rest and one-vs-one approaches. Evaluation commonly uses accuracy, precision and recall per class, and macro- and micro-averaged F1 scores, often visualized with a confusion matrix. Issues in moniluokkainen problems include class imbalance and overlapping decision boundaries, underscoring the need for appropriate metric selection.

Outside machine learning, the term can describe any context with several distinct categories—for example taxonomic schemes or educational categorization schemes that go beyond a single or two classes. In Finnish language usage, moniluokkainen is inflected to suit grammatical case and number as other adjectives do.