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  • Home
  • Predict ML
  • Getting Started
    • About Predict
    • Using Predict
    • Best practices
    • Prerequisites
    • Terminology
    • Data compliance and usage
  • Define a strategy
    • Decide what to predict
    • Attribute readiness
    • Prepare your data
    • Start training
  • Create training models
    • Add a model
    • Review your model
  • Evaluate and retrain models
    • Evaluate trained models
    • Strength scores
    • The Confusion Matrix
    • The ROC/AUC curve
    • Probability distribution
    • View model status
    • Retrain a model
  • Deploy models
    • Deploy a model
    • Undeploy a model
    • Deployed model health
    • Model retraining recommendations
    • Delete a model
  • Create audiences
    • Audience considerations
    • Create audiences with Predict
  • Advanced topics
    • Machine learning vs. artificial intelligence
    • Machine learning concepts and technology

Define a strategy

Decide what to predict

This article describes attributes to target, exclude, and output in Tealium Predict ML.

Attribute readiness

This article describes how to select the right target attribute to use in your models.

Prepare your data

This article describes data wellness concepts and actionable steps you can take to examine and optimize the readiness of your data layer before starting with Tealium Predict ML.

Start training

This article describes the steps required to start training your model.

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  • Glossary
  • Early Access
  • About Predict
  • Using Predict
  • Best practices
  • Prerequisites
  • Terminology
  • Data compliance and usage
  • Decide what to predict
  • Attribute readiness
  • Prepare your data
  • Start training
  • Add a model
  • Review your model
  • Evaluate trained models
  • Strength scores
  • The Confusion Matrix
  • The ROC/AUC curve
  • Probability distribution
  • View model status
  • Retrain a model
  • Deploy a model
  • Undeploy a model
  • Deployed model health
  • Model retraining recommendations
  • Delete a model
  • Audience considerations
  • Create audiences with Predict
  • Machine learning vs. artificial intelligence
  • Machine learning concepts and technology