Supervised Learning
Supervised Learning is a machine learning approach in which a model learns from labeled data, using known inputs and corresponding outputs to learn how to make predictions on new, unseen data.
What is Supervised Learning?
In supervised learning, each training example includes an input and a known target or label. The model learns the relationship between them by minimizing prediction errors during training. Common supervised learning tasks include classification, where the model predicts categories, and regression, where it predicts numerical values.
Why is Supervised Learning Important?
Supervised learning provides a structured way to train models for specific prediction tasks. When sufficient high-quality labeled data is available, it can produce accurate and measurable results and can be evaluated using metrics such as accuracy, precision, recall, and mean squared error.
Common use cases
Supervised Learning is commonly used in image classification, spam detection, fraud detection, sentiment analysis, medical diagnosis, demand forecasting, and other predictive applications.