Configure Your Use Case
Select machine learning models and configure feature extractors for use cases. Save computed features and prediction results in records.
Required Editions
| Available in: Lightning Experience |
- Prerequisites
Before you configure a use case, create a Einstein Discovery model and deploy the generated predictive model. If your use-case requires features to be computed in real time, configure an Apex class with the feature extraction logic. Determine the objects and fields that you want to store the computed features and prediction results in. - Select a Predictive Model
Select the predictive model that you deployed to get predictions for your use case. - Configure a Feature Extractor
For use cases that you create, configure feature extractors to compute features in real time or in batches, or upload sample features that the predictive model can use to generate predictions. For auto-generated use cases that require features computed in real time, you can extend the configuration of a partially suitable feature extractor by adding other feature extractors with your configured Apex classes. Or, you entirely update the configuration by using another feature extractor instead of the default feature extractor. The predictive model uses the features computed based on the configuration to generate predictions. - Specify the Maximum Number of Insights, Suggestions, and Recommendations
Specify the maximum number of suggestions, insights, and recommendations that you want to generate for your use case. Specify a percentage to show only those suggestions that improve the prediction score greater than that percentage. - Save Computed Features and Prediction Results
Save computed features, the prediction score, insights about the prediction score, and suggestions to improve the prediction score in records.
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