English: Diagram which explains the motivation and use of feature learning. In the paradigm, implicit feature representations are learned through various methods by inputting either raw data such as text, or an initial (usually sparse) feature set. The result is a richer, often lower dimensionality feature representation which can boost performance when used as the input for more specific learning tasks. Common tasks include classification and regression which generally require supervised learning and therefore labels to tune the models predictions.