# Feature Engineering In Machine Learning, a feature is an individual measurable property that serves as an input to a Model. A feature can be an attribute of the User, such as "customer\_since", or a processed or combined form of several attributes. The process of taking available data fields and transforming them into useful Features for the Model is known as Feature Selection. CrossEngage automatically retrieves data from Users and Events, and processes them into useful Features, which are then fed into the Model. ### Advanced Features The Features of transactions and activity can also be restricted in two dimensions; intervals (e.g "within the last 30 days") and categories (e.g "from product group A”). It is also possible to combine these restrictions, e.g "within the last 30 days from Product Group A". This means that additional features can be created by calculating features with restrictions. Crossengage's automatic data preparation already provides extended basic features for a certain set of categorical properties (always assuming that the information is available in the data!). #### By category The following additional Features can be calculated from the Transactions data. * The top 8 Categories of the field: productgroup\_id * The top 5 Categories of the field: order\_type * The top 8 Categories of the field: order\_channel * The top 3 Categories of the field: mapped\_order\_type The following additional Features can be calculated from the Activity data. * The top 5 Categories of the field: wtr\_type * The top 5 Categories of the field: activity\_type --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://documentation.crossengage.io/data-and-engagement-platform/prediction-models/feature-engineering.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.