What Is Supervised Learning?
Supervised learning is a type of machine learning where a model is trained on labeled data. This means that the dataset provided to the algorithm includes input-output pairs, where the inputs are features and the outputs are the corresponding labels or targets. The goal of supervised learning is to learn a mapping function from inputs to outputs so that the model can accurately predict the labels for new, unseen data. Common examples of supervised learning tasks include classification, where the goal is to assign inputs to predefined categories, and regression, where the aim is to predict continuous values. Supervised learning algorithms, such as linear regression, support vector machines, and neural networks, are widely used across various fields, including healthcare, finance, and natural language processing.