---
schema_version: "1"
id: d5190d8b97583848f19c7525009a2189e93379c98ef72e48a78c55278569e01f
title: Overall Performance Tab for Numeric Use Cases
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/analytics/264-0-0/bi_edd_model_metrics_overall_measure.md
canonical_url: https://help.salesforce.com/s/articleView?id=analytics.bi_edd_model_metrics_overall_measure.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: 341fff6651190dfbf42ce6edf1b1119a94734065bc96e9e8efd8ffb1a5f0c44c
description: The Overall Performance tab shows metrics for key indicators and model quality.
products:
  - CRM Analytics
  - Cross Cloud
release: "264"
---
# Overall Performance Tab for Numeric Use Cases

The Overall Performance tab shows metrics for key indicators and model quality.

![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-analytics-bi-264-0-0-production-enus/ec791473-f5b4-4f64-a656-9104f455d966/images/icon_note.png)

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.

## Navigate to the Overall Performance Tab

In Performance, click **Model Evaluation**, then **Overall Performance**.

![Overall Performance tab, showing predicted versus actual results](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-analytics-bi-264-0-0-production-enus/ec791473-f5b4-4f64-a656-9104f455d966/bi/images/bi/bi_edd_model_metrics_overall_measure.png)

## Summary Metrics

<table class="slds-table slds-table_bordered slds-max-medium-table_stacked slds-m-bottom_small ">
        <colgroup>
          <col style="width:19.193857965451055%">
          <col style="width:80.80614203454894%">
        </colgroup>
        <thead class="slds-text-title_caps ">
          <tr class="">
            <th scope="col" class="slds-cell-wrap" style="vertical-align:top;" id="d45826e66">Metric</th>
            <th scope="col" class="slds-cell-wrap" style="vertical-align:top;" id="d45826e69">Description</th>
          </tr>
        </thead>
        <tbody>
          <tr class="">
            <td data-label="Metric" class="slds-cell-wrap" style="vertical-align:top;" headers="d45826e66 ">
              <p style="margin-bottom:0;">R<sup>2</sup></p>
            </td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d45826e69 ">
              <p>R<sup>2</sup> measures the model's ability to
                explain variation in the outcome, which is an
                indicator of how predictive the model is.</p>
              <p>Range:</p>
              <ul>
                <li>0 means that the model is not able to explain
                  any variability in the outcome.</li>

                <li>1 means that the model explains all of the
                  variability.</li>

              </ul>
            </td>

          </tr>
          <tr class="">
            <td data-label="Metric" class="slds-cell-wrap" style="vertical-align:top;" headers="d45826e66 ">MAE</td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d45826e69 ">Mean Absolute Error. Measures the absolute
              difference between the actual value and the
              prediction. All differences are weighted equally in
              this average, which means that it is not as
              sensitive to outliers as MSE.</td>

          </tr>
          <tr class="">
            <td data-label="Metric" class="slds-cell-wrap" style="vertical-align:top;" headers="d45826e66 ">RMSE</td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d45826e69 ">Root Mean Squared Error. Represents the square root
              of MSE (Mean Squared Error, which is the average
              squared error of the model’s predictions). RMSE
              measures the difference between the values predicted
              by the model and the observed values. You can think
              of RMSE as the "standard deviation of
              errors".</td>

          </tr>
        </tbody>
      </table>

## Predicted vs Actual

Use this scatter plot to visually compare the model's predicted outcomes with actual outcomes. The closer the points are to the line of regression, the more accurate the model.

![Scatter plot, showing predicted versus actual results](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-analytics-bi-264-0-0-production-enus/ec791473-f5b4-4f64-a656-9104f455d966/bi/images/bi/bi_edd_model_metrics_overall_measure_predicted_actual.png)

## Residuals

Use this scatter plot to see the difference between the actual and predicted values (residuals), by predicted value. The points must appear randomly scattered. Points at 0 on the y-axis represent predictions that were exactly correct, while points above 0 were too low, and points below 0 were too high.

Choose the type of residuals you want to see. **Residuals** shows the difference between the actual and predicted values, or the raw residual values. **Standardized Residuals** shows raw residual values divided by the standard deviation.

![Tip](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-analytics-bi-264-0-0-production-enus/ec791473-f5b4-4f64-a656-9104f455d966/images/icon_tip.png)

Tip Standardized residuals are useful for identifying outliers.

![Scatter plot, showing predicted versus actual results](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-analytics-bi-264-0-0-production-enus/ec791473-f5b4-4f64-a656-9104f455d966/bi/images/bi/bi_edd_model_metrics_overall_measure_residuals.png)

## Normal QQ Plot for Standardized Residuals

For regression models, one of the key assumptions is that the residual errors for the outcome variable are normally distributed. Use the QQ (quantile-quantile) plot to quickly check this assumption and determine whether and how residual errors depart from normality.

![Quantile-quantile plot, showing the distribution of residual errors](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-analytics-bi-264-0-0-production-enus/ec791473-f5b4-4f64-a656-9104f455d966/bi/images/bi/bi_edd_model_metrics_overall_measure_qq.png)

If the QQ plot shows your residual errors to be approximately linear, then you can be confident that your model satisfies the normal distribution assumption.

#### See Also

- [Metrics for Numeric Use Cases](https://help.salesforce.com/s/articleView?id=analytics.bi_edd_model_metrics_measure.htm&release=264.0.0&type=5 "The numeric use case is based on outcomes that are numeric variables. Model Performance shows quality statistics associated with linear regression models.")