Classification

Single-label classification is a type of classification task where each instance in the dataset is assigned to one and only one class label. This is in contrast to multi-label classification, where instances can belong to multiple classes simultaneously.

Single-label classification is a type of supervised learning where each data instance is assigned exactly one label from a set of possible categories. This is the most common form of classification and is widely used when classes are mutually exclusive. The goal is to build a model that can accurately predict the correct class for new, unseen data points. Algorithms for single-label classification include logistic regression, support vector machines, and decision trees. This approach is straightforward to interpret and evaluate, making it suitable for many practical applications such as spam detection and image recognition.

Multi-label classification is a type of classification task where each instance can be assigned multiple labels simultaneously. This is different from single-label classification, where each instance is assigned only one label.

Multi-label classification is a supervised learning task where each instance can be assigned multiple labels simultaneously. Unlike single-label classification, where each data point belongs to one exclusive category, multi-label problems involve overlapping classes. This makes the problem more complex and requires specialized algorithms like neural networks with multiple outputs or binary relevance methods. Multi-label classification is useful in scenarios where data points naturally belong to multiple categories at once, such as tagging images or recommending products. Handling label correlations and dependencies is an important aspect of these models.