10. Compute Feature Table
In this tutorial, you will learn how to generate a Feature Table from a feature list created in previous tutorials. Once the table is generated, we will train a LightGBM model on the new training data.
Step 1: Compute Feature Table¶
Navigate to the Feature List Catalog under the Experiment section of the menu.
For the 'Suggested SHAP selection (1+2) after adjusted ideation' feature list, follow these steps:
-
Click
.
-
Select the Observation Table
Applications up to Sept 2024 with Loan Defaults
. -
Confirm the computation by clicking
.
Step 2: Review Feature Table¶
- Navigate to the Feature Table Catalog under the Experiment section of the menu.
- Click on the
Suggested SHAP selection (1+1) after adjusted ideation - Applications up to Sept 2024 with Loan Defaults
table. - Open the 'Preview' tab to examine the table and select individual columns by clicking on
.
Step 3: Train a Model¶
Download the files lgbm_credit_default_ui_tutorials.ipynb and modeling_script.py here. Use the notebook to evaluate the accuracy.
Step 4: Iterate¶
The AUC of a model trained on applications up to March 2024 and validated on the next 6 months is 0.80.
Now, it's your turn!
Suggestions:
- Train the model with the other feature lists in the catalog and create new ones.
- Refine the ideation process, such as additional filters for BUREAU table.
- Use the SDK to customize features.
- Use the holdout observation table to further validate the model.