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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

Getting Started with Predict ML

About Predict

This article describes the Tealium Predict ML product and how it is used to create, train, and deploy machine learning models to make predictions about visitor behavior.

Using Predict

This article provides an overview of the Tealium Predict ML workflow, basic Predict implementation, and best practices for readying your data and creating models.

Best practices

This article lists best practices that can help you get started selecting your target attribute, readying your data, and using the models that you create with Predict ML.

Prerequisites

This article describes what is required to use the Tealium Predict ML product, suggested steps to take before you begin to ensure ideal results, and available services.

Terminology

This article defines general statistical modeling terminology, terms specific to Tealium products, and terms used in the Tealium Predict ML interface.

Data compliance and usage

This article describes data compliance as it pertains to General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) and how data is used, shared, and stored when using Tealium Predict.

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  • 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