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            Configure Text Variables

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            Configure Text Variables

            Configure settings for individual text variables in your model.

            Required Editions

            Note
            Note Einstein Discovery stories are now models. We wish we could snap our fingers to update the name everywhere, but you can expect to see the previous name in a few places until we replace it.
            Available in Salesforce Classic and Lightning Experience.
            Available with CRM Analytics, which is available for an extra cost in Enterprise, Performance, and Unlimited Editions. Also available in Developer Edition.
            User Permissions Needed
            To configure text fields in a model: Create and Update Einstein Discovery Models
            1. On the Model Settings page, click a text variable. The variable panel appears.
              Alerts tab for a number field
            2. In the Alerts tab, respond to any suggestions regarding data issues for this field, such as high correlation, strongest predictors, or missing values. For more information, see Handle Quality Alerts.
              Edit Variable pane, showing the Alerts tab
            3. In the Performance tab, see how well your model works for each value within the selected variable.

              Performance is shown as a decimal score, where 1 is perfect accuracy. For example, in the variable Store, Miami has a performance score of 0.99, and Honolulu has a score of 0.79. You can then conclude that the model performs better for the Miami store than the Honolulu store.

              Performance tab for a number variable
              1. Optionally, if using a binary classification or regression model, click Row Count Analysis to see a detailed comparison of the values by performance and row count.
                Row Count Analysis chart for a number variable in a binary classification or regression model
              2. Optionally, if using a multiclass classification model, see the performance of each value by outcome.
                • To expand a value to see performance by outcome, click Actions button.
                  • To see a graphic comparison of values and outcomes by performance and row count, click Row Count Analysis.
                    • Use the dropdown to select different outcomes (1). The chart updates according to the selected outcome.
                    • Toggle between Actual Class and Predicted Class (2). The Actual Class groups data based on the observed value. The Predicted Class groups data based on the predicted value.
                    Row Count Analysis chart for a number variable in a multiclass model
                • To see a graph of performance by value and outcome, click Detailed Analysis. The darker the circle, the better the performance. The bigger the circle, the higher the row count.
                  Detailed Analysis chart for a number variable in a multiclass model
            4. In the Settings tab, configure the following settings.
              Edit Variable pane, showing the Settings tab
              Setting Description
              Analyze for bias

              Select this option to exclude a variable from the model so that it does not influence predictions and recommendations. If selected, Einstein Discovery shows a shield icon next to the title of the insight to remind you it’s a sensitive variable.

              Shield icon for sensitive fields

              This option allows you to evaluate and assess the variable’s impact in the model. Einstein Discovery still notifies you if it shows a 50% or higher correlation with the model’s outcome variable.
              Transform

              Select the method to transform your text:
              Include Only Select the values you want to include in the model. Depending on the following options, excluded values are either omitted from analysis or merged into the Other category.
              Histogram A bar chart graph shows the number of values that occur across values.

              Changes take effect after you create the model.

            • Apply Fuzzy Matching for Smarter Categorizations
              Fuzzy matching adds uniformity to spelling variations in variables, resulting in smarter categorizations and better predictions. Einstein Discovery transforms the data at prediction time, enabling you to skip cleansing during data prep.
            • Detect Sentiment in Unstructured Text
              Process unstructured data during model creation and categorize sentiment as positive, negative, or neutral.
            • Use Text Clustering to Analyze Unstructured Data
              Text clustering reduces unstructured data to its top keywords and enables you to quickly reveal hidden insights and improve decision making.
            • Impute Missing Text Values
              Einstein Discovery allows you to impute missing text values in your dataset. Enable imputation on a text variable, and Einstein Discovery automatically replaces missing values with data derived from another subset of your data.
             
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