Enrichment

Definition of Enrichment

Enrichment: In data science, enrichment is the process of expanding or enhancing a dataset with additional information. This can be done in order to improve the accuracy of predictions or to gain a deeper understanding of the data. Enrichment can be performed manually, by adding new data points to the dataset, or automatically, by using algorithms to extract new features from the data.

What is Enrichment used for?

Enrichment is a process used in data science and machine learning that involves augmenting an existing dataset with additional information. This is done to make the dataset more useful for analysis, by adding relevant details or features which can then be used to improve the accuracy and effectiveness of predictive models or other analytics tasks. Enrichment can also be used for feature engineering, where new features are created from existing ones or extracted from external data sources, such as weather data or demographic information. In addition to improving the accuracy of predictions, enrichment can also help to reduce bias in machine learning models by adding contextual data that helps to identify any potential correlation between variables. For example, when predicting customer churn rates, it may be beneficial to include geographical information in order to understand if customers from certain regions are more likely to leave than others. This type of enrichment can then be used to create targeted marketing campaigns tailored specifically towards these areas in order to reduce churn rates overall.