> For the complete documentation index, see [llms.txt](https://docs-old.evidentlyai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs-old.evidentlyai.com/user-guide/customization/feature-importance.md).

# Feature importance in data drift

{% hint style="info" %}
**You are looking at the old Evidently documentation**: this API is available with versions 0.6.7 or lower. Check the newer docs version [here](https://docs.evidentlyai.com/introduction).
{% endhint %}

You can add feature importances to the dataset-level data drift Tests and Metrics:

* `DataDriftTable`
* `TestShareOfDriftedColumns`

## Code example

Notebook example on showing feature importance:

{% embed url="<https://github.com/evidentlyai/evidently/blob/ad71e132d59ac3a84fce6cf27bd50b12b10d9137/examples/how_to_questions/how_to_add_feature_importances_to_drift.ipynb>" %}

## Compute feature importances

By default, the feature importance column is not shown. To display them, you must set the `feature_importance` parameter as `True`.

```python
report = Report(metrics = [
    DataDriftTable(feature_importance=True)
])
```

If you do not specify anything else, Evidently will train a random forest model using the provided dataset and derive the feature importances.

**Notes**:

* This is only possible if your dataset contains the `target` column.
* If you have both `current` and `reference` datasets, two different models will be trained. You will have two columns with feature importance: one for `reference` and one for `current` data.
* If your dataset also contains the `prediction` column, you should clearly label it using Column Mapping to avoid it being treated as a feature.

### Pass your own importances

You can also pass the list of feature importances derived during the model training process. This is a recommended option.

In this case, pass it as a list using the `additional_data` parameter when running the Report.

```python
report = Report(metrics = [
    DataDriftTable(feature_importance=True)
])
report.run(reference_data=reference,
           current_data=current.loc['2011-01-29 00:00:00':'2011-02-07 23:00:00'],
           column_mapping=column_mapping,
           additional_data = {'current_feature_importance':
              dict(map(lambda i,j : (i,j), numerical_features + categorical_features, regressor.feature_importances_))
            }
           )
```

You can pass the `current_feature_importance` – a single column will appear in this case. You can also optionally pass `reference_feature_importance`.