Metrics for Binary Classification Use Cases

The binary classification use case is based on text (categorical) variables with binary outcomes. Model Performance shows quality statistics associated with logistic regression models.

• Overview Tab for Binary Classification Use Cases
  The Overview tab shows an at-a-glance summary of your model’s quality.
• Model Evaluation Tab for Binary Classification Use Cases
  The Model Evaluation tab provides information about a model's performance, gains and lift, cross-validation results, and coefficient values.
• Threshold Evaluation for Binary Classification Use Cases
  Threshold Evaluation helps you optimize the threshold value for a model. The threshold value tells your model how to classify a binary outcome. If the calculated probability is above the threshold value, Einstein classifies the outcome one way (such as True or Positive). If the calculated probability is below the threshold value, Einstein classifies the outcome the other way (such as False or Negative).
• Prediction Examination Tab for Binary Classification Use Cases
  The Prediction Examination tab allows you to interact with the model metrics and see how they stack up against actual outcomes.
• View the R Code
  The R code contains the transformations made to your CRM Analytics dataset and scoring code. Inspect the code to examine the underlying details of the model that Einstein Discovery produced. R code is available for Binary Classification models.