Singlelabel

Single-label classification is a supervised learning task in which each instance is assigned exactly one label from a predefined set of categories. This contrasts with multi-label classification, where an instance may be associated with multiple labels simultaneously, and with binary classification, where the label set has two categories. In single-label, multiclass problems, the goal is to choose the most appropriate single class for each input. The label set is fixed and mutually exclusive, so the predicted label does not involve selecting additional labels for the same instance.

Common methods include logistic regression, support vector machines, decision trees, random forests, and neural networks. Training typically uses a softmax activation with cross-entropy loss across all classes, or one-vs-rest schemes for certain algorithms. These models learn to map input features to a probability distribution over the classes and select the highest-probability class at inference time.

Evaluation uses accuracy and, when necessary, class-specific metrics such as precision, recall, and F1 score. Confusion matrices are often used to analyze errors, with macro- and micro-averaged variants helping compare performance across imbalanced datasets.

Datasets commonly used for single-label classification include MNIST and CIFAR-10 for vision, and 20 Newsgroups for text. Real-world tasks often remain single-label, but some domains require multi-label outputs (for example, image tagging or multi-disease diagnosis), in which case different modeling approaches are used.

Key challenges include class imbalance, label noise, feature extraction for high-dimensional data, and choosing among architectures as the label set grows. Advances focus on improved representations, regularization, and scalable training for large datasets.