luokkalabeli

Luokkalabeli refers to the category assigned to each data instance in supervised learning, serving as the target variable that models aim to predict. In classification tasks, data points are labeled with one or more classes. In single-label multiclass classification, each instance receives exactly one label from a finite set (for example, digits 0–9 or animal species such as cat, dog, horse). In multilabel classification, an instance can have multiple labels simultaneously (for instance, a photo containing both a bicycle and a person).

Representations of class labels vary: as integers, as one-hot vectors, or as binary indicator vectors for all possible labels. The choice affects model training and evaluation. During training, models learn to map input features to predicted labels; during evaluation, predicted labels are compared with true labels using metrics such as accuracy, precision, recall, F1 score, or area under the precision-recall curve.

Label quality is crucial. Datasets rely on carefully defined labeling guidelines and may require multiple annotators to reach consensus. Label noise or inconsistency can degrade performance, so processes like adjudication, regular audits, and active-learning can help.

Ethical and practical considerations include avoiding leakage of test labels into training data, handling sensitive labels with privacy protections, and documenting labeling schemas for reproducibility. Tools and platforms exist to help create, manage, and quality-check labels, enabling consistent labeling across large datasets.