Supervised learning

PrepNuggets

In supervised learning, the input and output data are labelled to allow the algorithm to learn how to map inputs to their desired output.   The trained ML algorithms are then given new data to predict outcomes or recognise patterns.  For example, a stock prediction machine can be trained using past data like price history, company fundamentals, and economic data as inputs, and the past returns of various stocks as output training data.  The trained model can then be used to predict future stock returns using the latest data.

Compare: Unsupervised learning