---
schema_version: "1"
id: f2605cca8b8276e8dc6f6aaf5c2570b2448e1ae80c65af883401f5042f14d78b
title: Cross Validation 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_cross_validation_measure.md
canonical_url: https://help.salesforce.com/s/articleView?id=analytics.bi_edd_model_metrics_cross_validation_measure.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: 50af50278154cf2cffd8d32dcd5864efc43eb71862c4d98e25ea5ec03eb42ede
description: To test a model’s ability to make predictions, Einstein Discovery uses k-fold cross-validation, a process that reduces sampling bias when validating a model. This tab summarizes the results of the cross-validation process for this model, as well as some of the underlying computational details.
products:
  - CRM Analytics
  - Cross Cloud
release: "264"
---
# Cross Validation Tab for Numeric Use Cases

To test a model’s ability to make predictions, Einstein Discovery uses k-fold cross-validation, a process that reduces sampling bias when validating a model. This tab summarizes the results of the cross-validation process for this model, as well as some of the underlying computational details.

![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 Cross Validation Tab

In Performance, click **Model Evaluation**, then **Cross Validation**.

![Cross Validation tab, showing 4-fold cross-validation 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_cross_validation_measure.png)

## Columns on the Cross Validation Tab

 
| Column Name | Description |
| --- | --- |
| Metric Name | Name of the metric. |
| Training Set | Metrics for the set of observations in the CRM Analytics dataset that Einstein Discovery used to train the model. |
| Validation Set | Metrics for the set of observations in the CRM Analytics dataset that Einstein Discovery used to validate the predictions generated by the trained model. |
| Fold #1 | Metrics for the first fold. |
| Fold #2 | Metrics for the second fold. |
| Fold #3 | Metrics for the third fold. |
| Fold #4 | Metrics for the fourth fold. |

## Metrics on the Cross Validation Tab

The Cross Validation tab shows the following metrics for a model.

<table class="slds-table slds-table_bordered slds-max-medium-table_stacked slds-m-bottom_small ">
        <colgroup>
          <col style="width:17.36111111111111%">
          <col style="width:82.63888888888889%">
        </colgroup>
        <thead class="slds-text-title_caps ">
          <tr class="">
            <th scope="col" class="slds-cell-wrap" style="vertical-align:top;" id="d146836e154">Metric Name</th>
            <th scope="col" class="slds-cell-wrap" style="vertical-align:top;" id="d146836e156">Description</th>
          </tr>
        </thead>
        <tbody>
          <tr class="">
            <td data-label="Metric Name" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e154 ">Number of rows</td>
            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e156 ">
              <p>Total number of observations. The meaning of a
                value varies per column.</p>
              <ul>
                <li>For the <strong class="uicontrol">Training Set</strong>
                  and <strong class="uicontrol">Validation Set</strong> columns,
                  the numbers are the same. This value represents
                  the total number of observations in the entire
                  dataset used in the creation of the model.</li>
                <li>For the <strong class="uicontrol">Fold #</strong>1 through
                  <strong class="uicontrol">Fold #4</strong> columns, this value
                  represents how many observations fell in that fold
                  (approximately 25% of the entire dataset).
                </li>
                <li>For the <strong class="uicontrol">Average</strong> column,
                  this value represents the average of the folds,
                  which is the number of observations in the dataset
                  divided by 4.</li>
              </ul>
            </td>
          </tr>
          <tr class="">
            <td data-label="Metric Name" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e154 ">
              <p style="margin-bottom:0;">R<sup>2</sup></p>
            </td>
            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e156 ">
              <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 (random
                  chance).</li>
                <li>1 means that the model explains all of the
                  variability (perfect model).</li>
              </ul>
            </td>
          </tr>
          <tr class="">
            <td data-label="Metric Name" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e154 ">MSE</td>
            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e156 ">Mean Squared Error. Measures the average squared
              error of the model’s predictions. MSE computes the
              square difference between the observed (actual)
              outcome and the predicted values, and then averages
              them.</td>
          </tr>
          <tr class="">
            <td data-label="Metric Name" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e154 ">RMSE</td>
            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e156 ">Root Mean Squared Error. Represents the square root
              of MSE. RMSE measures the difference between the
              values predicted by the model and the observed
              (actual) values. You can think of this value as the
              “standard deviation of errors.”</td>
          </tr>
          <tr class="">
            <td data-label="Metric Name" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e154 ">MAE</td>
            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e156 ">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 Name" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e154 ">RMSLE</td>
            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e156 ">
              <p>Root-Mean-Squared Logarithmic Error. Compare with RMSE:</p>
              <ul>
                <li>RMSLE is less sensitive to outliers than RMSE.
                  RMSE penalizes only for the overall scale of the
                  error, while RMSLE penalizes for the scale of the
                  error relative to the actual value.</li>
                <li>RMSLE penalizes under-predictions more than
                  over-predictions. This factor makes RMSLE more
                  useful than RMSE when you want to err on the side
                  of over-estimating. For example, to optimize stock
                  levels of non-perishables, you prefer a surplus
                  over potentially running out of stock.</li>
              </ul>
            </td>
          </tr>
          <tr class="">
            <td data-label="Metric Name" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e154 ">Residual Deviance</td>
            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e156 ">
              <p>Measures how well the developed model performs on
                your dataset.</p>
              <ul>
                <li>As with Null Deviance, lower values mean
                  better fit. Thus, if Residual Deviance is low, the
                  model fits the data well.</li>
                <li>The larger the gap between Residual Deviance
                  and Null Deviance, the better the model is doing
                  at picking up the complexities in the data.</li>
              </ul>
            </td>
          </tr>
          <tr class="">
            <td data-label="Metric Name" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e154 ">Mean Residual Deviance</td>
            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e156 ">
              <p style="margin-bottom:0;">Measures how well the model performs on your
                dataset. Lower values indicate a better fit.</p>
            </td>
          </tr>
          <tr class="">
            <td data-label="Metric Name" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e154 ">Null Deviance</td>
            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e156 ">Measures how well a simple model performs on your
              dataset. Lower values mean a better fit. Thus, if
              Null Deviance is low, your data is not particularly
              complex.</td>
          </tr>
          <tr class="">
            <td data-label="Metric Name" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e154 ">Null Degrees of Freedom</td>
            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e156 ">Represents the Chi-Square distributions of Null
              Deviance.</td>
          </tr>
          <tr class="">
            <td data-label="Metric Name" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e154 ">Residual Degrees of Freedom</td>
            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d146836e156 ">Represents the Chi-Square distributions of Residual
              Deviance.</td>
          </tr>
        </tbody>
      </table>

## Model Validation Methodology

Einstein Discovery conducts k-fold cross-validation (k=4) on your model. This process involves the following steps:

- Randomly divide all the observations in the CRM Analytics dataset into four separate partitions of equal size.
- Conduct four test passes (folds) in which three of the partitions serve as the training set and one partition serves as the test set.
    
    ![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 After completing the four test passes, each partition has served one time as the validation set and three times as part of the training set.
- For each fold, compile model metrics.
- Take the average of the four folds for an overall score.

#### 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.")