# Cluster Insights

> Cluster Insights - Learn how the Cluster Insights visualization in Workbench helps you to understand
> the natural groupings in your data for predictive experiments.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-04-24T16:03:56.749795+00:00` (UTC).

## Primary page

- [Cluster Insights](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/cluster-insights.html): Full documentation for this topic (HTML).

## Sections on this page

- [Visualization controls](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/cluster-insights.html#visualization-controls): In-page section heading.
- [Select clusters](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/cluster-insights.html#select-clusters): In-page section heading.
- [Rename clusters](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/cluster-insights.html#rename-clusters): In-page section heading.
- [Change or create feature lists](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/cluster-insights.html#change-or-create-feature-list): In-page section heading.
- [Search](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/cluster-insights.html#search): In-page section heading.
- [Download CSV](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/cluster-insights.html#download-csv): In-page section heading.
- [View more features](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/cluster-insights.html#view-more-features): In-page section heading.
- [Clusters and features](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/cluster-insights.html#clusters-and-features): In-page section heading.

## Related documentation

- [NextGen UI documentation](https://docs.datarobot.com/en/docs/workbench/index.html): Linked from this page.
- [Workbench](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/index.html): Linked from this page.
- [Predictive experiments](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/index.html): Linked from this page.
- [Evaluate models](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/index.html): Linked from this page.
- [clustering experiment](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-predictive/ml-unsupervised.html#clustering): Linked from this page.
- [Feature Impact](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/feature-impact.html): Linked from this page.
- [custom feature list reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/custom-list-ref.html): Linked from this page.
- [CSV contains](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/cluster-insights-classic.html#download): Linked from this page.

## Documentation content

# Cluster Insights

| Tab | Description |
| --- | --- |
| Explanations | Supports using clustering to capture a latent feature in the data, to surface and communicate actionable insights quickly, or to identify segments in the data for further modeling. |

To analyze the clusters in your data, after building a [clustering experiment](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-predictive/ml-unsupervised.html#clustering), select a model from the Leaderboard and open the Cluster Insights visualization.

> [!NOTE] Note
> The maximum number of features computed for Cluster Insights is 100. The features are selected from the features used to train the model, based on the [Feature Impact](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/feature-impact.html) (high to low). The remaining features (those not used to train the model) are sorted alphabetically.

The following table describes the Cluster Insights visualization.

|  | Element | Description |
| --- | --- | --- |
| (1) | Visualization controls | Provides tools for working with the display. |
| (2) | Clusters and features | Provides cluster and feature details, including visualizing cluster breakdown by feature and listing features, sorted by feature importance. The Informative Feature list displays by default; use the Feature list dropdown in the controls to change the display. |

## Visualization controls

Use the controls in the top bar to work with the display.

### Select clusters

Use Select clusters to add or remove clusters from the visualization view (not from the experiment). The visualization supports a maximum of five clusters per screen (use the arrow on the far right )

Click + Add cluster to display additional clusters; delete a cluster from the display with the trash can. To reorder clusters, click a cluster in a position and re-assign a new cluster to that position.

### Rename clusters

You can rename clusters after you gain an understanding of what they represent. The cluster names propagate to other insights and predictions, allowing you to further analyze the clusters. Click Rename clusters, edit cluster names, and click Finish editing when done.

### Change or create feature lists

By default, DataRobot builds clustering models using the Informative Features list. Select another feature list, either automatically generated or custom, to explore a different subset of features. Changing the list does not impact the model, only the display; however, analyzing the features not used to generate the clusters can still be useful to answer questions like "How does income distribute among my clusters, even if I'm not using it for clustering?"

See the [custom feature list reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/custom-list-ref.html) for information on creating new lists.

### Search

Use Search to show an individual feature's placement in each cluster.

### Download CSV

Click Download CSV to download the cluster insights. The [CSV contains](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/cluster-insights-classic.html#download) the information displayed in the Cluster Insights visualization, and more detailed feature data.

### View more features

Features display, for each displayed cluster, in order of Feature Impact, most important to least by default. Four features display by default; click the number to adjust the number of features displayed per page. To navigate through the features, click the right arrow above the clusters.

## Clusters and features

Clusters are comprised of groups of similar features that form natural segments. The Clustering Insights visualization helps to understand how those groups were formed. See the reference documentation for details on [investigating cluster features](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/cluster-insights-classic.html#investigate-cluster-features).

Clusters display in columns, showing the features in the cluster and the feature impact score and values for each feature. The visualization helps to evaluate the distribution of features across clusters. Cluster sizes are shown as percentages above the [cluster name](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/cluster-insights.html#rename-clusters). The All data cluster contains 100% as a baseline comparison.

- Click the arrow to the right of the cluster names to scroll through cluster.
- Click the Impact column name to reverse the order.

Hover on a feature within a cluster to see details for the top four features.

Expand a row to see additional features or statistics, depending on feature type, within a cluster.

For numeric features:

For categorical features, see a histogram showing the top four features and all others bucketed into `Other`:

To drill into all categories, click the gear :fontawesome-gear: next to the feature name and select High cardinality. Hover on a value how many records in the selected cluster contain that value.