Create a Model from Scratch

Create a model that uses historical data to predict future outcomes. Select and structure your data source and define the desired outcome. Then train the model to reveal predictive inferences and insights by using AI, machine learning, and statistical analysis.

1. Choose Type
   Based on your use case, select a binary, regression, or multiclass (beta) model.

   

   

   Note

   Classification models are a pilot or beta service that is subject to the Beta Services Terms at Agreements - Salesforce.com or a written Unified Pilot Agreement if executed by Customer, and applicable terms in the Product Terms Directory. Use of this pilot or beta service is at the Customer's sole discretion.

2. Select Data
   Select the source of data to train your model with. Einstein uses the data to train and test the model.
   When selecting a data source, consider some guiding questions.

   Question	Guidance
   What outcome (such as a KPI) do you want to predict?	Predictive modeling detects patterns related to three types of use cases: regression (continuous numbers), binary classification (two-value text outcomes), or multiclassification (from 3 through 10 possible outcomes). Ideal data candidates for predictive modeling often involve KPIs associated with large volumes of data and many business decisions.
   Which variables do you want to include?	Deciding which variables to include impacts the quality and interpretability of a predictive model. When variables are meaningful and relevant, it becomes simpler for stakeholders and business users to understand the relationships between factors and trust the predicted outcomes.
   Where can you find this information?	Understanding where the data comes from is valuable in assessing quality, identifying bias, and adhering to data governance and compliance.

3. Select Training Data
   Use all records in the data source to train the model. Or, specify conditions to restrict the records used to train the model.
   1. To select a specific subset of data to train the model, click Filtered Set of Records.
   2. Define your filter condition.
      

      

      Important

      Filter your training set to use less than 20 million rows.

      Logic	Description
      Condition Requirements	All Conditions Are Met—if all conditions are true, the filter is applied. If one of the conditions is false, the filter isn’t applied. Any Condition Is Met—if one condition is true, the filter is applied.
      Field	Based on the data source. The data attribute or variable to use when applying the filter.
      Operator	Defines the relationship between the field and condition. The available operators depend on the field type.
      Value	Specific value to assess the field. For text fields, select one or multiple values. For numeric fields, enter a value. For date fields, use the calendar to select a date.

4. Set Goal
   Select a business problem that you want to solve. Examine the key performance indicators (KPIs) you want to improve. Explore which candidate KPIs can benefit the most from deploying an AI-powered solution.
   1. Select the field that you want to predict values for. Decide which outcome variable you want to explore, and at what granularity. The outcome variable could be a KPI value (such as revenue, discount, cost measure, or duration) or other quantifiable outcome. You can also use categories (text fields) with two values (binary) as an outcome variable.
   2. Choose to maximize or minimize your goal. Einstein builds and trains a model focused on maximizing or minimizing the outcome variable. For example, your goal can be to maximize net margin or minimize customer churn. Einstein builds a model focused on achieving higher net margin or reducing customer churn.
   
5. Prepare Data
   Select which fields to include as variables in the model. Make sure that the variables are relevant to the business outcome you want to predict. Refine the data to provide better results by using variables that are accurate, complete, and representative of real-world business operations in terms of quantity (volume) and variation (diversity). Don’t use fields that contain sensitive data or personally identifiable information (PII).

   You can also turn on Autopilot so Einstein can automatically select variables with the highest impact on your prediction and use case.

   

   Important We support up to 50 text variables (fields) with 100 unique values or more. A data model object with text variables that exceed this limit may be dropped or not be visible during model setup.

   
6. Select Algorithm
   The algorithm is the approach, or the computational procedure, the model uses to learn from the data and make predictions. You can also turn on Automatic Selection, so Einstein can automatically select an algorithm with the highest impact on your prediction and use case.
   1. Choose the algorithm for your model.
      

      Algorithm	Description
      GLM	Default. Generalized Linear Model (GLM) is an equation-based algorithm that typically completes quickly. It works best when the relationship between the variables and the outcome is relatively simple. Only GLM can produce explanations based on the interaction between variables, such as the combined impact of region and month together.
      GBM	Gradient Boosting Machine (GBM), is a tree-based algorithm where the decision trees are built sequentially to better fit the data. It handles data complexity such as different relationships between variables and varied data distributions better, compared to GLM.
      XGBoost	Extreme Gradient Boosting (XGBoost), is an extension of GBM that’s optimized for efficiency. It's a tree-based algorithm where groups of decision trees are built sequentially to better fit the data, while avoiding overfitting.

7. Review and Train
   Train your model after reviewing the details.

   
   1. Review the details of the model. Optionally, click the pencil icon to edit a step.
      

      Note

      You can edit all steps when creating a model. After you create the model, you can't edit steps such as: Choose Type, Select Data, and Set Goal.

   2. Click Save to enter a name and an optional description for your model, and click Save & Train to create the model.