Multi-Class Classification

Multi-class classification is a supervised machine learning task where the goal is to assign input data points to one of three or more discrete categories or classes. It involves training a model on labeled data to predict the correct class for new, unseen instances, with each instance belonging to exactly one class. This is a fundamental problem in fields like image recognition, natural language processing, and medical diagnosis.

Also known as: Multiclass Classification, Multi-Class Classification, Multiclass, Multi-Class, MCC

Why learn Multi-Class Classification?

Developers should learn multi-class classification when building applications that require categorizing data into multiple distinct groups, such as spam detection (spam, not spam, promotional), sentiment analysis (positive, negative, neutral), or object recognition in images (cat, dog, bird). It is essential for tasks where binary classification (two classes) is insufficient, enabling more nuanced and practical predictions in real-world scenarios.

Learning Resources

• docs: Scikit-learn Documentation: Multiclass and Multilabel Classification
• course: Coursera: Machine Learning by Andrew Ng
• tutorial: Towards Data Science: Multi-Class Classification Tutorial with Scikit-Learn
• video: YouTube: Multi-Class Classification Explained
• book: Book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow