This article describes target, output, and excluded attributes in Tealium Predict ML and how to select the right target attribute to use in your models.
In this article:
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.
Configurations should not be changed in the attribute. If you need to change an attribute configuration, create a new attribute to collect the required data.
When you create a model using Tealium Predict, you can choose from a list of potential target attributes from your AudienceStream dataset. Each attribute is given a rating of Ready or Not Ready to signify whether the attribute is ready for training. The readiness of an attribute displays next to each attribute in the drop-down list of available attributes when you click + New Model. This feature helps you determine, in advance, whether an attribute candidate is a Ready or Not Ready to use as a target output attribute, as shown in the following example.
This rating simplifies model creation by clarifying which of your flags and badges are ready to be used to create models. A rating of Not Ready does not mean that the attribute is problematic in other contexts, but that it is currently deemed insufficient for successful training of a Tealium Predict model.
Machine learning technology generally requires a high volume of data to succeed and machine learning models provide better results when trained on a large amount of data.
The following two factors to define target attributes that are Ready or Not Ready:
Both the true and false groups must be above a minimum threshold. For example, for the dates that span the Training Date Range, the median daily counts of True and False visitors must be greater than or equal to 200. This threshold is intentionally set as low as possible to provide the most options possible for target attributes. A model with a target attribute that is labeled Not Ready typically fails during the training process due to insufficient data for the model.
If the target attribute you want to use is labeled as Not Ready, try one of the following solutions: