binary classification

**Binary Classification**

Binary classification is a fundamental type of supervised learning in machine learning and data science, where the goal is to categorize data points into one of two distinct classes or categories. Common examples include spam vs. non-spam email detection, disease vs. no disease diagnosis, and fraud vs. legitimate transaction identification. This technique involves training a model on labeled data and then using it to predict the class of new, unseen instances. Popular algorithms for binary classification include logistic regression, support vector machines (SVM), decision trees, and neural networks. Understanding binary classification is essential for solving many practical problems in various fields such as finance, healthcare, marketing, and more.