# View model insights
URL: https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-model-insights.html

> Feature Impact shows which features are driving model decisions the most. It is calculated using permutation or SHAP.

# View model insights

On the Registry > Models tab, the Insights tab is available for [DataRobot](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-register-dr-models.html) and [custom models](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-register-cus-models.html) (not agent-monitored [external models](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-register-ext-models.html)). The following insights are supported:

**Feature Impact:**
[https://docs.datarobot.com/en/docs/images/registry-model-insights-1.png](https://docs.datarobot.com/en/docs/images/registry-model-insights-1.png)

**Individual Prediction Explanations:**
[https://docs.datarobot.com/en/docs/images/registry-model-insights-2.png](https://docs.datarobot.com/en/docs/images/registry-model-insights-2.png)

**SHAP Distributions: Per Feature:**
[https://docs.datarobot.com/en/docs/images/registry-model-insights-3.png](https://docs.datarobot.com/en/docs/images/registry-model-insights-3.png)

**Lift Chart:**
[https://docs.datarobot.com/en/docs/images/registry-model-insights-4.png](https://docs.datarobot.com/en/docs/images/registry-model-insights-4.png)

**ROC Curve:**
[https://docs.datarobot.com/en/docs/images/registry-model-insights-5.png](https://docs.datarobot.com/en/docs/images/registry-model-insights-5.png)

**Residuals:**
[https://docs.datarobot.com/en/docs/images/registry-model-insights-6.png](https://docs.datarobot.com/en/docs/images/registry-model-insights-6.png)

**Confusion Matrix:**
[https://docs.datarobot.com/en/docs/images/registry-model-insights-7.png](https://docs.datarobot.com/en/docs/images/registry-model-insights-7.png)


| Insight | Description | Problem type | Sliced insights? |
| --- | --- | --- | --- |
| Feature Impact | Shows which features are driving model decisions. | All | ✔ |
| Individual Prediction Explanations | Estimates how much each feature contributes to a given prediction, with values based on difference from the average. | Binary classification, regression | ✔ |
| SHAP Distributions: Per Feature | Displays, via a violin plot, the distribution of SHAP values and feature values to aid in the analysis of how feature values influence predictions. | Binary classification, regression | ✔ |
| Lift Chart | Depicts how well a model segments the target population and how capable it is of predicting the target. | All | ✔ |
| ROC Curve | Provides tools for exploring classification, performance, and statistics related to a model. | Binary classification | ✔ |
| Residuals | Provides scatter plots and a histogram for understanding model predictive performance and validity. | Regression | ✔ |
| Confusion matrix | Compares actual with predicted values in multiclass classification problems to identify class mislabeling. | Classification, time-aware |  |

> [!NOTE] Compute insights
> When you first access the Insights tab for a registered model version, to view the table of predictions for the Individual Prediction Explanations insight and the violin chart for the SHAP Distributions: Per Feature insight, click Compute.

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