# Feature lists
URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/custom-list-ref.html

> Create machine learning and time series experiments and iterate quickly to evaluate and select the best predictive and forecasting models.

# Feature lists

Feature lists control the subset of features that DataRobot uses to build models and make predictions. You can use one of the [automatically created lists](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/custom-list-ref.html#automatically-created-feature-lists) or [create a custom feature list](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/custom-list-ref.html#create-custom-feature-lists) by manually adding features. You can also review, rename, and delete custom feature lists.

You might want to use feature lists to:

- Remove features that cannot be used in the model for any reason, for example, a feature that is causing target leakage.
- Make predictions faster by removing unimportant features (i.e., ones that don't improve the model's performance).

## Automatically created feature lists

> [!NOTE] Time-aware feature lists
> The information below applies to non-time-aware feature lists. For information on time-aware feature lists, see [Time series feature lists](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/ts-feature-lists.html).

DataRobot automatically creates several feature lists for each dataset and experiment. Note that:

- Time series feature lists differ from predictive feature lists.
- Features created from a search for interactions result in different lists (appended with a plus (+) sign).
- An experiment's target feature is automatically added to every feature list.

The following describes the automatically created feature lists for non-time series experiments:

| Feature list | Description | Availability |
| --- | --- | --- |
| All Features | While not a feature list (not available for use to build models), the All Features selection sets the Project Data display to list all columns in the dataset as well as any additional transformed features. |  |
| Informative Features | The default feature list if DataRobot does not detect target leakage. This list includes features that pass a "reasonableness" check that determines whether they contain information useful for building a generalizable model. For example, DataRobot excludes features it determines are low information or redundant, such as duplicate columns, a column containing all ones or reference IDs, a feature with too few values, and others. | After EDA1 |
| Informative Features - Leakage Removed | The default feature list if DataRobot detects target leakage. This list excludes feature(s) that are at risk of causing target leakage and any features providing little or no information useful for modeling. To determine what was removed, you can see these features labeled in the Data table with All Features selected. | After EDA1 if target leakage is detected |
| Informative Features + | If Autopilot is set to run on the Informative Features list and Search for interactions is enabled, DataRobot creates Informative Features +, which may not have the same number of features as the original because when deriving the new feature from the old, keeping both may result in redundancy. If that is the case, DataRobot removes one of the parent features. | (Classic only) After EDA2 with Search for interactions enabled |
| Raw Features | All features in the dataset, excluding user-derived features and including those excluded from the Informative Features list (e.g., duplicates, high missing values). | After EDA1 |
| Univariate Selections | Features that meet a certain threshold (an ACE score above 0.005) for non-linear correlation with the selected target. DataRobot calculates, for each entry in the Informative Features list, the feature’s individual relationship against the target. | After EDA2 |
| DR Reduced Features | A subset of features, selected based on the Feature Impact calculation of the best non-blender model on the Leaderboard. DataRobot then automatically retrains the best non-blender model with this DR Reduced Features list, creating a new model. DataRobot compares the original and new models, selects the better one, and retrains this model at a higher sample size for model recommendation purposes. DR Reduced Features, in most cases, consists of the features that provide 95% of the accumulated impact for the model. If that number is greater than 100, only the top 100 features are included. If redundant feature identification is supported in the project, redundant features are excluded from DR Reduced Features. | After EDA2, but not for Quick mode |

## Create custom feature lists

> [!NOTE] Required permissions
> To create feature lists, you must have Owner or Editor access to the dataset.

If you do not want to use one of the [automatically created](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/custom-list-ref.html#automatically-created-feature-lists) feature lists, you can create customized feature lists and train your models on them to see if they yield a better model.

The ability to create a custom feature list is available from:

| Location | Description |
| --- | --- |
| Pre-modeling / After EDA1 |  |
| Data tab in Registry | Create custom feature lists for registered datasets prior to being added to a Use Case and used for modeling. From here, you can also perform variable type transformations on single features. |
| Data explore page | Create custom feature lists for Use Case datasets after profiling the dataset but prior to modeling. Feature lists created at this stage appear in experiments based on the dataset. |
| Post-modeling / After EDA2 |  |
| Data preview tile | Post-modeling features for predictive modeling and derived modeling data for time-aware modeling. |
| Feature lists tile | Automatically created and custom lists available for the experiment. |
| Feature Impact insight | Option for impact-based feature selection (predicitive only). |
| Cluster Insights | Change the insight display or create lists from predictive clustering experiments. |

Note that lists created from an experiment are:

- Used, within an experiment, for retraining models or training new models from the blueprint repository .
- Available only within that experiment, not across all experiments in the Use Case.
- Not available in the data explore page.

### Add features

To create a custom feature list, navigate to one of the tabs or insights listed in the table above and click + Create feature list.

Then, you can:

- Select features individually.
- Use bulk actions to select multiple features.

#### Select features individually

**Non-time series:**
To select features individually:

Use the
Show features from
dropdown to change the displayed features that are available for selection. The default display lists features from the
Raw Features
list. All automatically generated and custom lists are available from the dropdown.
Use the checkbox to the left of the feature name to add or clear selections.
(Optional) Use the search field to update the display to show only those features, within the
Show features from
selection, that match the search string.
Save the list
.

**Time series:**
> [!NOTE] Note
> You must include the [ordering feature](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-time-aware/ts-datetime.html#2-set-ordering-feature) when creating feature lists for time series model training. The ordering feature is not required if the list is not used directly for training, such as [monotonic constraint](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/custom-list-ref.html#monotonic-feature-constraints) lists.

To select features individually:

Use the
Show features from
dropdown to change the displayed features that are available for selection. The default display list features from the
Time Series Extracted Features
list. All automatically generated and custom lists are available from the dropdown.
(Optional) If you are using the new feature list to train models, you must add the ordering feature by clicking
+ Add ordering feature
or selecting the checkbox to the left of the feature.
Use the checkbox to the left of the feature name to add or clear selections.
Save the list
.


#### Bulk feature list actions

To add multiple features at a time, choose a method from the Bulk selection dropdown:

**Select by variable type:**
Use Select by variable type to create a list containing all features from the dataset that are of the selected variable type. While you can only select one variable type, afterwards, you can individually add any other features (of any type).

[https://docs.datarobot.com/en/docs/images/wb-custom-fl-2-ts.png](https://docs.datarobot.com/en/docs/images/wb-custom-fl-2-ts.png)

**Select by existing feature list:**
Use Select by existing feature list to add all features in the chosen list.

[https://docs.datarobot.com/en/docs/images/wb-custom-fl-3.png](https://docs.datarobot.com/en/docs/images/wb-custom-fl-3.png)

Note that the bulk actions are secondary to the Show features from dropdown. For example, showing features from "Top5" lists the five features added in your custom list. If you then use Select by existing feature list > Informative features (or Time Series Informative Features), all features in "Top5" that are also in "Informative Features" are selected. Conversely, if you Show features from: Informative Features and Select by existing feature list > Top4, those five features are selected.

[https://docs.datarobot.com/en/docs/images/wb-custom-fl-7.png](https://docs.datarobot.com/en/docs/images/wb-custom-fl-7.png)

**Select N most important:**
Use Select N most important to add the specified number of "most important" features from the features available in the list select in the Show features from dropdown. The [importance score](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html#importance-score) indicates the degree to which a feature is correlated with the target—representing a measure of predictive power if you were to use only that variable to predict the target.

[https://docs.datarobot.com/en/docs/images/wb-custom-fl-4.png](https://docs.datarobot.com/en/docs/images/wb-custom-fl-4.png)


### Save feature list

Once all features for the list are selected, optionally rename the list and provide a description in the Feature list summary. The summary also provides count and type of features included in the list.

Then, click the Create feature list button to save the information. The new list will display in the listing on the Feature lists tab.

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