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.
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!).
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