# Predict ML ## About markdown versions All pages in this area are available in clean markdown format. To access the markdown version of any page, append `/index.md` to the page URL. Example: - HTML: `https://docs.tealium.com/iq-tag-management/getting-started/what-is-tealium-iq/` - Markdown: `https://docs.tealium.com/iq-tag-management/getting-started/what-is-tealium-iq/index.md` ## Predict ML - [About Predict](https://docs.tealium.com/predict/getting-started/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](https://docs.tealium.com/predict/getting-started/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](https://docs.tealium.com/predict/getting-started/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](https://docs.tealium.com/predict/getting-started/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](https://docs.tealium.com/predict/getting-started/terminology/): This article defines general statistical modeling terminology, terms specific to Tealium products, and terms used in the Tealium Predict ML interface. - [Decide what to predict](https://docs.tealium.com/predict/strategy/decide-what-to-predict/): This article describes attributes to target, exclude, and output in Tealium Predict ML. - [Attribute readiness](https://docs.tealium.com/predict/strategy/attribute-readiness/): This article describes how to select the right target attribute to use in your models. - [Prepare your data](https://docs.tealium.com/predict/strategy/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. - [Add a model](https://docs.tealium.com/predict/training-models/add-a-model/): This article describes how to add a model, select a target attribute, an output attribute, and exclude attributes from a model. - [Review your model](https://docs.tealium.com/predict/training-models/review-your-model/): This article describes optional review steps and how to initiate the first training for your model. - [Start training](https://docs.tealium.com/predict/strategy/start-training/): This article describes the steps required to start training your model. - [Evaluate trained models](https://docs.tealium.com/predict/evaluate-models/evaluate-trained-models/): This article provides an overview of how to evaluate your trained version before deploying your model. Use the following sections as a guide to view model status and the potential strength of predictions for your model before you deploy. - [Strength scores](https://docs.tealium.com/predict/evaluate-models/strength-scores/): This article provides detailed information about model scoring techniques and formulas used to assign scores and ratings to trained and deployed models in Tealium Predict ML. - [The Confusion Matrix](https://docs.tealium.com/predict/evaluate-models/the-confusion-matrix/): This article describes the Confusion Matrix and how to use it to evaluate a trained model. - [The ROC/AUC curve](https://docs.tealium.com/predict/evaluate-models/the-roc-auc-curve/): This article describes the ROC/AUC curve and how to use it as a performance measurement of a trained model. - [Probability distribution](https://docs.tealium.com/predict/evaluate-models/probability-distribution/): This article describes how to use the probability distribution to interpret trained models. - [View model status](https://docs.tealium.com/predict/evaluate-models/view-model-status/): This article describes how to view your model status. - [Retrain a model](https://docs.tealium.com/predict/evaluate-models/retrain-a-model/): This article describes how to retrain a model after evaluating the model and determining changes that are required to improve predictions. - [Deploy a model](https://docs.tealium.com/predict/deploy/deploy-a-model/): This article describes how to deploy one or more versions of a trained model. - [Undeploy a model](https://docs.tealium.com/predict/deploy/undeploy-a-model/): This article describes how to undeploy one or more versions of a trained model. - [Deployed model health](https://docs.tealium.com/predict/deploy/deployed-model-health/): This article provides explanations of the metrics and ratings available when using Tealium Predict ML that help you understand the quality of your deployed models. - [Model retraining recommendations](https://docs.tealium.com/predict/deploy/model-retraining-recommendations/): This article provides best practices and recommendations for retraining a model. - [Delete a model](https://docs.tealium.com/predict/deploy/delete-a-model/): This article describes how to delete a model. You cannot delete a version of a model, only an entire model. - [Audience considerations](https://docs.tealium.com/predict/audiences/audience-considerations/): This article serves as a guideline of items to consider when creating audiences using results from Tealium Predict. - [Create audiences with Predict](https://docs.tealium.com/predict/audiences/enrich-your-output-attribute/): This article describes how to use your predictions to create one or more audiences. - [Machine learning vs. artificial intelligence](https://docs.tealium.com/predict/advanced/machine-learning-vs-artificial-intelligence/): This article provides a generic overview of the differences between machine learning and artificial intelligence. - [Machine learning concepts and technology](https://docs.tealium.com/predict/advanced/machine-learning-concepts-and-technology/): This article describes Machine Learning technology concepts, goals, audiences, and technological advances.