Binary Classification

Binary classification is a supervised machine learning task where the goal is to predict one of two possible discrete outcomes or classes for a given input. It involves training a model on labeled data to distinguish between two categories, such as spam vs. not-spam, fraud vs. legitimate, or positive vs. negative sentiment. This is a fundamental problem in machine learning and statistics, often serving as a building block for more complex classification tasks.

Also known as: Two-class classification, Dichotomous classification, Binary prediction, Binary logistic regression, 0/1 classification

Why learn Binary Classification?

Developers should learn binary classification when building predictive models for scenarios with two distinct outcomes, such as in email filtering, medical diagnosis (e.g., disease present or not), or customer churn prediction. It is essential for applications in natural language processing, computer vision, and fraud detection, where clear decision boundaries are needed. Mastering binary classification provides a foundation for understanding multi-class classification and other advanced machine learning techniques.

Learning Resources

• docs: Scikit-learn Documentation: Binary Classification
• course: Coursera: Machine Learning by Andrew Ng
• tutorial: Towards Data Science: Binary Classification Tutorial
• tutorial: Kaggle: Titanic - Machine Learning from Disaster