supervised learning

Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset, meaning that the input data is paired with the correct output or target variable. The goal of supervised learning is to learn a mapping between the input and output variables so that the algorithm can make accurate predictions on new, unseen data.

Supervised learning algorithms can be used for a variety of tasks, such as classification, where the output variable is a categorical label, or regression, where the output variable is a continuous value.

Examples of supervised learning include predicting the price of a house based on its features (regression), classifying emails as spam or not (classification), or recognizing handwritten digits (classification).