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AI Accelerator and Scoring Framework
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          Filter Your Training and Scoring Datasets

          Filter Your Training and Scoring Datasets

          Focus your predictions on a specific subset of the object data by defining filter conditions. Get more useful predictions by excluding the records that are irrelevant to your prediction. Make sure that you have sufficient records in your training and scoring datasets by counting the records. Filtering is optional. You can use the complete dataset for training and scoring.

          Required Editions

          Available in: Lightning Experience
          User Permissions Needed
          To set up a Data Cloud template configuration: AI Accelerator User
          1. From Setup, in the Quick Find box, enter Industries Cloud Einstein, and then select Scoring Framework.
          2. On the card of the template configuration that you want to use, click Quick actions menu button, and select Edit.
          3. For Filter Your Training and Scoring Datasets, click Set Up.
          4. Add conditions by using the object’s fields to filter your training and scoring datasets.
            You can choose to apply separate filter conditions for training and scoring, or use a single filter for both. Use the toggle to select your option.
          5. To determine the number of training and scoring records, click Count Records.
            If you have an insufficient number of records, edit your conditions, and recount the number of examples and scoring records.
          6. To continue to define the template configuration, click Save & Continue.
            To return to the Scoring Framework Setup page, save your changes. If you don’t want to filter training and scoring datasets, click Clear Selections.
          Example
          Example

          A financial institution uses Scoring Framework to get predictions about customers in the US who are likely to purchase more assets. The financial institution’s Salesforce admin adds conditions to filter accounts with the billing country as US.

           
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