Supervised Learning

Supervised learning is a type of machine learning where models are trained on labeled datasets, meaning each input has a corresponding correct output. The algorithm learns to map inputs to outputs by identifying patterns and relationships within the training data. This approach is commonly used for tasks like classification, regression, and prediction, including applications such as spam detection, image recognition, and credit scoring. The model’s accuracy improves as it processes more examples and adjusts based on errors during training. Supervised learning forms the foundation of many practical AI systems that rely on structured data and clear feedback signals.