Machine Learning Composite Attributes

Machine Learning Composite Attributes

📘 Composite Attributes

Composite Attributes are attributes that are used to define a relationship between two or more key attributes.

If all keys in the composite types appear in an attribute combination then all those keys are replaced with composite values. If any of the keys in the composite attributes show up in the attribute combination than that key is replaced with the composite value.

Composite attributes in Machine Learning

Assuming that the base level attributes are Product, origin, customer, delivery, and customertype.

Let assume the composite attributes are Delivery and Customer type, with a dependent attribute of SalesRegion

This means wherever we have Delivery and customertype, it can be replaced with SalesRegion.

  1. A composite attribute would be displayed when the composite attributes are a subset of the base level attributes.
  2. When a dependent attribute of the composite is selected, then the independent composite attributes are replaced with the dependent attribute even when none of the key attributes are selected.

  1. When any key of the independent composite attributes are selected then any combination of the selected keys are replaced with the dependent attributes

  1. Where a stand-alone attribute is among the list of independent composite attributes then the independent /dependent attributes could be a stand-alone attribute. Here in the below case, the Product is a standalone attribute, hence the rest attributes are listed as standalone.

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