Creating a feature group in a project

Editing a feature group

When you have selected the columns of the data asset to be used in the feature group, you can then view each feature and edit it to specify the role it will have in Machine Learning models.

1. Click a feature name and click Edit this feature. A window opens displaying the following tabs:

   • Details - provide the following information about the feature. Select a Role to be assigned to the feature:

     • Input: the feature can be used as input for training a Machine Learning model.
     • Target: the feature to be used as the prediction target when the data is used to train a Machine Learning model.
     • Identifier: the primary key, such as customer ID, used to identify the input data.

     Enter a Description, Recipe (any method or formula used to create values for the feature) and any Tags.

   • Value descriptions Value descriptions allow you to clarify the meaning of specific values. For example, consider a column "credit evaluation" with the values -1, 0 and 1. You can use value descriptions to provide meaning for these values. For example, -1 might mean "evaluation rejected". You can enter descriptions for particular values. For numerical values, you can also specify a range. To specify a range of numerical values, enter the following text [n,m] where n is the start and m is the end of the range, surrounded by square brackets, and click Add. For example, to describe all age values between 18 and 24 as "millenials", enter [18,24] as the value and millenials as the description. If you have a profile defined, the profile values are displayed in the value descriptions list. From here you can select one value or multiple values.

     

   • Fairness information You can define Monitor or Reference groups of values for monitoring bias. The values that are more at risk of biased outcomes can be placed in the Monitor group. These values are then compared to values in the Reference group. To specify a range of numerical values, enter the following text [n,m] where n is the start and m is the end of the range, surrounded by square brackets. For example, to monitor all age values between 18 and 35, enter [18,35]. Then select Monitor or Reference and click Add. You can also specify Favorable outcomes. See Fairness in AutoAI experiments for more information about fairness.

2. When you have edited the feature, click Save. You can now see your changes in the Feature Details window. Close this window to return to the feature group.

Removing features from a group

To remove a feature from a group:

1. Preview the asset in the project and select the Feature group tab.
2. In the Features table that is displayed, select the feature (or features) that you want to remove.
3. In the toolbar that appears, select Remove from group.

The feature, or feature group if you selected all the features, is removed.

Searching for a feature group

You can search for assets or columns across all projects. To filter your search results to find assets with a feature group, select Data to see the filter options, and select Feature group. Assets containing a feature group will then be listed in the search results.

Using the Python API to create and use feature groups

You can also use the assetframe-lib Python library in notebooks to create and edit feature groups. This library also allows you use feature metadata like fairness information when creating machine learning models.