Supervised Learning

Supervised learning is a machine learning technique where a model is trained on labeled data.

It is used when the goal is to predict outcomes or classify data based on input-output pairs. Supervised learning algorithms are commonly applied in tasks such as classification and regression. These algorithms work by learning from a training dataset that includes both the input data and the corresponding correct output.

For example, in a classification task, a supervised algorithm might be trained to recognize images of cats and dogs by learning from a labeled dataset of cat and dog images. In a regression task, it might predict house prices based on features like size, location, and number of bedrooms.

Supervised algorithms can be used in specific ways to reduce the effort required to label training data. Semi-supervised learning involves a training phase as supervised learning does, but not all the training data is labelled. Active learning is a special type of semi-supervised learning where the algorithm itself determines which training data the user is to label to achieve the best training results.

Supervised learning is important because it provides a clear framework for training models to make accurate predictions. It is a fundamental approach in machine learning, enabling the development of models that can generalize well to new, unseen data.