---
schema_version: "1"
id: 6c042a6a246a33a4db91b7dbe39027454fd4376e7974fbaf26bbf698aadb3e54
title: Cross Validation Tab for Multiclass 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_cross_validation_multiclass.md
canonical_url: https://help.salesforce.com/s/articleView?id=analytics.bi_edd_model_metrics_cross_validation_multiclass.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: 0608cbe20139cdf5f127e380bf6cb82b8f68093e0e337450559ef3217b8c6342
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 Multiclass Classification 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 Perfomance, click **Model Evaluation**, then click **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_multiclass.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 uses to train the model. |
| **Validation Set** | Metrics for the set of observations in the CRM Analytics dataset that Einstein Discovery uses 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

<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="d106296e185">Metric Name</th>
            <th scope="col" class="slds-cell-wrap" style="vertical-align:top;" id="d106296e188">Description</th>
          </tr>
        </thead>
        <tbody>
          <tr class="">
            <td data-label="Metric Name" class="slds-cell-wrap" style="vertical-align:top;" headers="d106296e185 ">
              <strong class="uicontrol">Number of Rows</strong>
            </td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d106296e188 ">
              <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>
              </ul>
            </td>

          </tr>
          <tr class="">
            <td data-label="Metric Name" class="slds-cell-wrap" style="vertical-align:top;" headers="d106296e185 ">
              <strong class="uicontrol">Log Loss</strong>
            </td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d106296e188 ">Logarithmic Loss. Measures model performance on a
              scale of 0–1, where 0 represents a perfect model
              (the predicted probability correctly matches actual
              observations 100%). The less the predicted
              probability correctly matches actual observations
              (lower performance), the higher the log loss. Log
              loss considers uncertainty in model
              performance.</td>

          </tr>
          <tr class="">
            <td data-label="Metric Name" class="slds-cell-wrap" style="vertical-align:top;" headers="d106296e185 ">
              <strong class="uicontrol">Mean Per Class Error</strong>
            </td>

            <td data-label="Description" class="slds-cell-wrap" style="vertical-align:top;" headers="d106296e188 ">Measures how often the predictions are wrong. Lower
              values mean the predictions are wrong less often,
              and therefore the model is better at making
              predictions.</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 Multiclass Classification Use Cases](https://help.salesforce.com/s/articleView?id=analytics.bi_edd_model_metrics_multiclass.htm&release=264.0.0&type=5 "The multiclass classification use case is based on categorical variables with 3–10 possible outcomes. The Model Metrics tabs show quality statistics associated with multiclass models.")