Single-Label Classification

Binary classification is a type of classification task where each instance in the dataset is assigned to one of two possible classes. This is a fundamental problem in machine learning and can be approached using various algorithms, each with its strengths and weaknesses.

Binary classification is a type of supervised learning where the model categorizes input data into one of two possible classes. It is one of the simplest and most common classification tasks, often used when the output is a yes/no, true/false, or presence/absence decision. Algorithms for binary classification learn from labeled data to distinguish between the two classes based on input features. This approach is widely applied in fields such as medical diagnosis, spam detection, and fraud detection. The performance of binary classifiers can be evaluated using metrics like accuracy, precision, recall, and F1-score.

Multi-class classification is a type of classification task where each instance can be assigned to one of three or more classes. This is different from binary classification, where each instance is assigned to one of two classes.

Multi-class classification is a supervised learning task where the goal is to categorize input data into one of three or more classes. Unlike binary classification, where there are only two possible outcomes, multi-class classifiers must distinguish among multiple categories. These algorithms learn decision boundaries that separate the different classes based on training data. Common techniques include one-vs-rest, one-vs-one strategies, and direct multi-class algorithms like neural networks and random forests. Multi-class classification is widely used in image recognition, natural language processing, and many other domains requiring more detailed categorization.