Feature Discovery¶

To deploy AI across the enterprise and make the best use of predictive models, you must be able to access relevant features. Often, the starting point of your data does not contain the right set of features. Feature Discovery discovers and generates new features from multiple datasets so that you no longer need to perform manual feature engineering to consolidate various datasets into one.

See the Feature Discovery file requirements for information about dataset sizes, and the associated considerations for important additional information.

Open Feature Discovery¶

To perform Feature Discovery in Workbench, in the Data tab, click the more options icon > Feature Discovery to the right of the dataset that will serve as the primary dataset. When you add and configure secondary datasets in the Feature Discovery recipe, you will define their relationship to the dataset selected here.

DataRobot opens Feature Discovery and adds the primary dataset to the canvas.

Configure primary dataset settings¶

With the primary dataset selected, enter the Target (what you want to predict) and Prediction point (time of the prediction). Prediction point is only available if a date feature is detected in the dataset.

Then, click Save—Primary data settings saved is displayed at the bottom of the page.

Add secondary datasets¶

Feature Discovery requires at least one secondary dataset. Otherwise, you do not need to perform Feature Discovery and can use the single dataset to directly set up an experiment. To add secondary datasets:

1. Click + Add Datasets in the left panel. The Add Data modal opens.

   

2. You can add data from a data connection, the Data Registry, or your current Use Case, as well as preview a dataset by clicking it. Select the box to the left of each secondary dataset you want to add, then click Add Datasets.

   

   All secondary datasets are displayed in the left panel.

Add relationships¶

Adding a relationship between datasets tells DataRobot that the two datasets are connected. There are two ways to establish a relationship between a primary and secondary dataset:

• Select the secondary dataset, and click the + that appears below a dataset node on the canvas.

  

• Select a dataset node on the canvas and from more options , select Add relation. In the left panel, select the dataset you want to join.

  

Set join conditions¶

While adding a relationship establishes that there's a connection between two datasets, the join conditions specify how they're related.

If the tables in your datasets are well-formed, DataRobot automatically detects compatible features and populates the Join condition field with the most appropriate feature, typically, a feature that's included in both datasets.

For more information, see Set join conditions in the DataRobot Classic section.

Configure secondary dataset settings¶

Select a secondary dataset node on the canvas to configure its settings, including its name, feature list, and time-awareness (if applicable).

Node Settings¶

To edit the settings for a secondary dataset node, click on a secondary dataset node and open the Node Settings tab, which includes the following options:

Time-awareness¶

If DataRobot detects a date feature in the primary dataset, you can select a prediction point to configure time-awareness. To edit these settings for a secondary node, open the Time-awareness tab, which includes the following options:

For more information, see Time-aware feature engineering.

Review relationship configurations¶

After configuring at least one secondary dataset, you can test the quality of those relationship configurations to identify and resolve potential problems early in the creation process. The Relationship Quality Assessment tool verifies join keys, dataset selection, and time-aware settings.

Click Review configuration to test the relationships on the Feature Discovery canvas.

Each node displays the results of the assessment. If the quality of a relationship passes the assessment, a green check mark is displayed in the node.

If the assessment detects quality issues, a yellow exclamation point is displayed in the affected node.

For more information, see Test relationship quality.

Publish a Feature Discovery recipe¶

When you've finished configuring relationships and they've passed the relationship configuration assessment, click Publish in the upper-right corner to access additional settings, including Feature Discovery controls, partitioning, and the ability to rename the output dataset. The tabs below describe the options available:

When you're done, click Publish. DataRobot then performs joins and aggregations as part of Feature Discovery, generating a new output dataset that is then registered in the Data Registry and added to your current Use Case.

Next steps¶

From here, you can:

• Set up an experiment.
• Add more data.