Classification Models

Classification models are a type of supervised machine learning algorithm used to predict categorical labels or classes for input data. They analyze features of data points to assign them to predefined categories, such as spam vs. not-spam, disease diagnosis, or image recognition. These models are fundamental in data science and artificial intelligence for tasks involving discrete outcomes.

Also known as: Classification Algorithms, Classifiers, Supervised Classification, Categorization Models, ML Classification

Why learn Classification Models?

Developers should learn classification models when building applications that require automated decision-making based on patterns in data, such as fraud detection, customer segmentation, or natural language processing. They are essential for solving problems where the goal is to categorize inputs into distinct groups, enabling predictive analytics in fields like healthcare, finance, and marketing.

Learning Resources

• docs: Scikit-learn Classification Documentation
• course: Coursera Machine Learning Course by Andrew Ng
• tutorial: Towards Data Science: Classification in Machine Learning
• tutorial: Kaggle Tutorial on Classification