---
schema_version: "1"
id: ad122d5c6d89008c5475bfb511ffc3bb8e3fc8403216af4f5c36f591b70a2fcd
title: Overall Performance Tab for Binary Classification 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_category.md
canonical_url: https://help.salesforce.com/s/articleView?id=analytics.bi_edd_model_metrics_overall_category.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: be638325ec458bcc470249e14293766d87bd4d4335ba016b7c8fb8318b603715
description: The Overall Performance tab shows key summary metrics for model quality.
products:
  - CRM Analytics
  - Cross Cloud
release: "264"
---
# Overall Performance Tab for Binary Classification Use Cases

The Overall Performance tab shows key summary metrics for 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 accuracy analysis 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_category.png)

## Summary Metrics

<table class="slds-table slds-table_bordered slds-max-medium-table_stacked slds-m-bottom_small ">
        <colgroup>
          <col style="width:18.281535648994517%">
          <col style="width:81.71846435100548%">
        </colgroup>
        <thead class="slds-text-title_caps ">
          <tr class="">
            <th scope="col" class="slds-cell-wrap" style="vertical-align:top;" id="d91751e66">Metric</th>
            <th scope="col" class="slds-cell-wrap" style="vertical-align:top;" id="d91751e69">Description</th>
          </tr>
        </thead>
        <tbody>
          <tr class="">
            <td data-label="Metric" class="slds-cell-wrap" style="vertical-align:top;" headers="d91751e66 ">
              <strong class="uicontrol">AUC</strong>
            </td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d91751e69 ">
              <p>Area Under the Curve. Measures the logistic model’s
                rate of correct classification. AUC is frequently
                used to evaluate model quality in classification use
                cases.</p>
              <p>Range:</p>
              <ul>
                <li>0.5 means that the model performed no better
                  than random guessing.</li>
                <li>1.0 means that the model correctly classified
                  the data 100% of the time. An AUC of 1.0 is
                  suspect because it can indicate data leakage: the
                  data used to train your model includes one or more
                  columns that contain the information that you are
                  trying to predict.</li>
              </ul>
            </td>

          </tr>
          <tr class="">
            <td data-label="Metric" class="slds-cell-wrap" style="vertical-align:top;" headers="d91751e66 ">
              <strong class="uicontrol">GINI</strong>
            </td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d91751e69 ">
              <p>GINI Index. Measures how closely this
                classification model performs to a theoretically
                best possible model.</p>
              <p>Range:</p>
              <ul>
                <li>0 means that the model performed no better
                  than random guessing.</li>
                <li>1 means that the predictions matched
                  observations exactly (and must be viewed with
                  skepticism).</li>
              </ul>
            </td>

          </tr>
          <tr class="">
            <td data-label="Metric" class="slds-cell-wrap" style="vertical-align:top;" headers="d91751e66 ">
              <strong class="uicontrol">MCC</strong>
            </td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d91751e69 ">
              <p>Matthews Correlation Coefficient. Measures the
                quality of a classification model. Provides a more
                even representation of the four parts of the
                confusion matrix than other classification metrics.
                In contrast, accuracy and the F1 score can be
                misleading when one class is predicted much more
                accurately than another in a classification use
                case.</p>
              <p>Range:</p>
              <ul>
                <li>-1 means that the model wrongly predicted the
                  opposite class every time.</li>
                <li>0 means that the model performed no better
                  than random guessing.</li>
                <li>+1 means that the model correctly predicted
                  the class every time.</li>
              </ul>
            </td>

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

## Accuracy Analysis

Accuracy measures the proportion of outcomes that the model predicted correctly.

Formula:

Accuracy = (TP + TN) / Total # of Predictions

where

- TP represents the number of true positives (positive prediction with a positive result)
- TN represents the number of true negatives (negative prediction with a negative result)
- Total # of Predictions that the model made (both correct and incorrect)

 
| Metric | Description |
| --- | --- |
| **The maximum accuracy for this model is** | The maximum accuracy for this model. |
| **True Positive Rate** | This model correctly scores records as true _n_% of the time. |
| **True Negative Rate** | This model correctly scores records as false _n_% of the time. |
| **False Positive Rate** | This model incorrectly scores records as true _n_% of the time. |
| **False Negative Rate** | This model incorrectly scores records as false _n_% of the time. |
| **Threshold Value** | The threshold value represents the tradeoff between the true positive and false positive rates. You can adjust this threshold setting in the [Threshold Evaluation for Binary Classification Use Cases](https://help.salesforce.com/s/articleView?id=analytics.bi_edd_model_metrics_threshold_evaluation_category.htm&release=264.0.0&type=5 "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)."). |

#### See Also

- [Metrics for Binary Classification Use Cases](https://help.salesforce.com/s/articleView?id=analytics.bi_edd_model_metrics_category.htm&release=264.0.0&type=5 "The binary classification use case is based on text (categorical) variables with binary outcomes. Model Performance shows quality statistics associated with logistic regression models.")