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Diagram of the feature learning paradigm in machine learning for application to downstream tasks, which can be applied to either raw data such as images or text, or to an initial set of features for the data. Feature learning is intended to result in faster training or better performance in task-specific settings than if the data was inputted directly, compare transfer learning.[1] In machine lear
This article needs to be updated. Please help update this article to reflect recent events or newly available information. (February 2024) Feature engineering is a preprocessing step in supervised machine learning and statistical modeling[1] which transforms raw data into a more effective set of inputs. Each input comprises several attributes, known as features. By providing models with relevant i
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