This document describes how access the Tiles view in Tealium Predict and use the sparkline graph as a guideline to view the ongoing health of a deployed model.
In this article:
The Tiles view displays a "tile" for each model that displays a snapshot of the prediction results for that model. To access the Tiles view, go to Predict > Overview.
From the Tiles view, you can view the status of a model, such as In Training or Deployed, in the upper right corner of each tile. Click any tile to view the details for a model and edit, if desired.
The detailed summary in each tile displays the strength rating for the deployed model and a "sparkline graph" as an overview. Strength ratings are automatically recalculated automatically and the sparkline updates to include the latest rating.
In the Tiles view, the strength score rating displays in parentheses as a decimal number, such as "(0.91)". This number shows the F1 Score on which the rating is based. This strength rating is based on the F1 score, which cannot be known or calculated for a deployed model until the initial prediction timeframe has elapsed.
For this reason, this strength rating is not available in the tile that displays for a particular model until the day after the prediction timeframe has elapsed. For example, if a model to predict the likelihood to purchase in the next 10 days is deployed on January 1st, the rating cannot be calculated until after January 10th.
The sparkline graph that displays in Tealium Predict is based on a simple trend graph. The graph displays an overview that depicts the ongoing health of a deployed model over time.
The sparkline graph adapts to versioning. For example, for a given model, if you first deploy Version 1 and later Undeploy Version 1 and Deploy Version 2, the graph automatically switches from Version 1 to Version 2. Adapting to versioning ensures you are provided with a continuous and realistic perspective of the strength of the output attribute for latest deployed version of the model.
See the following articles for additional information about model scores and evaluation: