This article describes attributes to target, exclude, and output in Tealium Predict ML.
When defining a model, each attribute in your Tealium AudienceStream CDP profile is reviewed to automatically determine the top attributes that have a predictive relationship for the action you want to predict.
A target attribute is a AudienceStream attribute that represents the visitor behavior that you want to predict with any Tealium Predict model. For example, for the boolean visit attribute “Has Purchased”, a value of true indicates that a purchase event has occurred during a visit while a value of false means a purchase event did not occur during a visit.
The target attribute must be either a boolean/flag or a badge type attribute and be Visit or Visitor-scoped.
Properly structured booleans default to false and are enriched to true. Visit booleans are reset to false after each visit, while Visitor booleans reset back to falsedepending on the attribute configuration.
You can exclude attributes that are not relevant for your model. When deciding which attribute types to exclude, Tealium recommends that you first train the model for initial insights with no attributes excluded.
Training without including exclusion attributes provides insight into which attributes the model finds the most relevant and can lead you to consider introducing new AudienceStream attributes to help future model trainings.
For example, after the initial training, you can exclude attributes with values that occur outside of the training period. After excluding these types of attributes, your training F1 score results may be lower when you retrain; however, your model produces better results when deployed.
- Attributes based on dates of visit or dates of purchase. These attributes do not repeat their values outside of the training period.
- Attributes based on unique user information, such as a User ID or Analytics ID. These attributes do not apply to other users outside of the training period.
The Output Attribute is created by default when a new model is created. It is a numeric Visit-scoped data-layer attribute that stores the Prediction value generated by a corresponding Deployed Model.
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This page was last updated: January 7, 2023