---
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title: Einstein Engagement Scoring Model Confidence
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/mktg/264-0-0/mc_anb_model_confidence.md
canonical_url: https://help.salesforce.com/s/articleView?id=mktg.mc_anb_model_confidence.htm&release=264.0.0&type=5
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content_type: documentation
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description: Model confidence is a way to express the relative strength of a prediction generated by a model. Understanding the concept can help you interpret Einstein Engagement Scoring for email model confidence scores.
products:
  - Marketing
  - Marketing Cloud Intelligence
release: "264"
---
# Einstein Engagement Scoring Model Confidence

Model confidence is a way to express the relative strength of a prediction generated by a model. Understanding the concept can help you interpret Einstein Engagement Scoring for email model confidence scores.

Model confidence provides the probability that a random positive sample has a higher probability of being positive than a random negative sample. Marketing Cloud Engagement uses the area under the Receiver Operating Characteristic curve, or a ROC curve, to evaluate the strength of subscriber engagement predictions. Scores are smoothed so they present an even, understandable view of your customer personas.

When model confidence is closer to 1.00, or 100%, there’s greater predictive strength for that engagement score. Lower model confidence indicates weaker predictive strength for that engagement score. The closer the confidence is to 0.50, or 50%, the more the model has about the same predictive strength of a coin toss. These ranges are the model confidence thresholds for Einstein Engagement Scoring.

- Excellent: 1.00–0.85
- Good: 0.84–0.75
- Fair: 0.65–0.50
- Poor: < 0.50

Each engagement prediction tile on the dashboard contains a model confidence indicator. The indicator consists of three circles that correspond with the model confidence thresholds:

- Excellent: three filled dots
- Good: two filled dots
- Fair: one filled dot
- Poor: no filled dots

Fair or poor model confidence can indicate insufficient subscriber data. It can also result from a change in email marketing strategy that influences subscriber engagement behaviors.

#### See Also

- [Einstein Engagement Scoring Dashboard](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_einstein_engagement_scores_dashboard.htm&release=264.0.0&type=5 "Use the dashboard to view engagement predictions for email and MobilePush channels, including the likelihood to click an email or spend time in a mobile app, and other key performance indicators (KPI). To enable Web Conversion scoring, implement Collect Tracking Code. You can analyze subscriber Persona changes over time.")
- [Engagement Score Thresholds](https://help.salesforce.com/s/articleView?id=mktg.mc_anb_engagement_scores_thresholds.htm&release=264.0.0&type=5 "Each key performance indicator (KPI) in Einstein Engagement Scoring has an Audience Health indicator. This indicator shows whether the average grade for that score across all your subscribers is Excellent, Good, Fair, or Poor.")