Binary classification is a type of supervised machine learning algorithm that involves categorizing data into one of two classes or categories. In binary classification, the goal is to predict which class a given input data sample belongs to. This type of classification is commonly used in a variety of applications such as spam detection, medical diagnosis, and credit scoring. The output of binary classification is typically a binary decision, such as "spam" or "not spam", "positive" or "negative", or "malignant" or "benign". The key challenge in binary classification is developing a model that accurately predicts the correct class labels for new data samples based on the features of the training data. Common algorithms used for binary classification include logistic regression, support vector machines, decision trees, and neural networks.