9. Create New Feature Lists and Models

In the previous tutorials, you successfully created two feature lists, which are now available in the Catalog.

In this tutorial, you will learn how to create new feature lists:

• using the Feature List Builder
• from a model’s Feature Importance

You’ll also learn how to train new models using these feature lists, or materialize features to train models outside FeatureByte.

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Step 1: Review Existing Feature Lists

1. Navigate to the Feature Lists Catalog under the Experiment section of the menu. Confirm that the two feature lists you previously created are listed.

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2. Click on the feature list suggested during ideation. ---

3. Go to the Features tab to review the features in the list.

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4. Open the Themes tab to identify signal types missing from the feature list. Once reviewed, close the window.

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Step 2: Create a New Feature List Using the Feature List Builder

1. Add the feature list suggested by ideation to the Feature List Builder by clicking .

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2. Review the Builder’s suggestions by clicking in the section at the bottom of the Feature List Builder.

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3. Click on for the CLIENT/BUREAU/MOST FREQUENT theme to explore associated features. Review EDA results and add the feature using the .

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4. Review the feature list by clicking and save it using . Name it “1 + Ideated Features.”

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5. Confirm the new feature list appears in the Catalog.

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Step 3: Create a New Feature List from a Model

1. Navigate to Leaderboard under the Experiment menu and configure the following:

   • Observation Table: Applications Q1 2025
   • Type: Validation
   • Metric: AUC

   

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2. Click on the best-performing model and open its Feature Importance tab.

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3. Select the Per Feature Panel and click . Set the Importance Threshold Percentage to 0.95. This will generate new features derived from dictionary features and create a feature list composed of the top features and keys for the model.

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4. Return to the Feature Lists Catalog to confirm the new list appears.

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Step 4: Train a New Model from a Feature List

1. Click to train a new model.

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2. Configure your model as follows:

   • Name: XGBoost with top 308 keys
   • Training Observation Table: Applications up to Dec 2024
   • Validation Observation Table: Applications Q1 2025

   You can review and edit parameters by clicking on them.

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3. Navigate to Tasks under the Manage menu to track model training progress.

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4. Once training completes, verify the new model appears on the leaderboard.

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5. (Optional) Select the new model and open its details page. Navigate to the Predict tab to generate predictions using a holdout Observation Table. Optionally, include feature values and SHAP values (either raw or normalized) alongside the predictions for deeper analysis.

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Step 5: (Optional) Compute a Feature Table

If you want to train a model outside FeatureByte, you can compute a Feature Table using the same feature list.

1. Return to the Feature List Catalog.

2. Click .

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3. Select the Observation Table: Applications with Credit Default target and confirm by clicking .

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4. Once materialization completes, navigate to the Feature Tables Catalog under Experiment to confirm creation.