6. Formulate Use Case

Why Create a Use Case?¶

Creating a Use Case in FeatureByte, while optional, offers significant benefits. It not only defines the goal and application of your prediction model but also helps organize your training data and improve post-deployment feature monitoring.

Plus, for FeatureByte Enterprise users, it's used by FeatureByte's Ideation engine to suggest features tailored to your use cases.

We'll create one Use Case for our Credit Default Dataset:

Loan Default by client: Predict clients' payment difficulties the next 6 months for new loan based on its early stage application data and prior credit history.

We will define a Context, establish a Target and then link the Use Case with the Context and Target.

In [1]:

import featurebyte as fb

# Set your profile to the tutorial environment
fb.use_profile("tutorial")

catalog_name = "Credit Default Dataset SDK Tutorial"
catalog = fb.Catalog.activate(catalog_name)

import featurebyte as fb # Set your profile to the tutorial environment fb.use_profile("tutorial") catalog_name = "Credit Default Dataset SDK Tutorial" catalog = fb.Catalog.activate(catalog_name)

16:59:23 | INFO     | SDK version: 3.2.0.dev66
INFO    :featurebyte:SDK version: 3.2.0.dev66
16:59:23 | INFO     | No catalog activated.
INFO    :featurebyte:No catalog activated.
16:59:23 | INFO     | Using profile: staging
INFO    :featurebyte:Using profile: staging
16:59:23 | INFO     | Using configuration file at: /Users/gxav/.featurebyte/config.yaml
INFO    :featurebyte:Using configuration file at: /Users/gxav/.featurebyte/config.yaml
16:59:23 | INFO     | Active profile: staging (https://staging.featurebyte.com/api/v1)
INFO    :featurebyte:Active profile: staging (https://staging.featurebyte.com/api/v1)
16:59:23 | INFO     | SDK version: 3.2.0.dev66
INFO    :featurebyte:SDK version: 3.2.0.dev66
16:59:23 | INFO     | No catalog activated.
INFO    :featurebyte:No catalog activated.
16:59:23 | INFO     | Catalog activated: Credit Default Dataset SDK Tutorial
INFO    :featurebyte.api.catalog:Catalog activated: Credit Default Dataset SDK Tutorial
16:06:14 | INFO     | No catalog activated.
16:06:14 | INFO     | Catalog activated: Grocery Dataset Tutorial

Create a Context¶

A Context defines the scope and circumstances in which features are expected to be served.

Let's describe the Context for our use case:

• Context Name: New Loan Application
• Description: Loan application under review.
• Primary Entity: New Application

In [2]:

context_name = "New Loan Application"
fb.Context.create(
    context_name,
    primary_entity=["New Application"],
    description="Loan application under review.",
)

context_name = "New Loan Application" fb.Context.create( context_name, primary_entity=["New Application"], description="Loan application under review.", )

Out[2]:

Context

name	New Loan Application
created_at	2025-10-15 08:59:24
updated_at	None
description	Loan application under review.
author	None
primary_entities	name serving_names catalog_name 0 New Application [SK_ID_CURR] Credit Default Dataset SDK Tutorial
default_eda_table	None
default_preview_table	None
associated_use_cases	[]

Creating a Target¶

For this use case, we'll use the descriptive approach, as the target cannot be derived directly from the data and must be provided alongside the observation tables.

For an example of a target computed by the SDK, check out the Grocery SDK Tutorial.

In [3]:

target_name = "Loan_Default"
target = fb.TargetNamespace.create(
    name=target_name,
    window="182d",
    dtype="INT",
    primary_entity=["New Application"],
    target_type=fb.TargetType.CLASSIFICATION,
)

target_name = "Loan_Default" target = fb.TargetNamespace.create( name=target_name, window="182d", dtype="INT", primary_entity=["New Application"], target_type=fb.TargetType.CLASSIFICATION, )

Create a Use Case¶

Let's link our Use Case with the Context and Target.

In [4]:

use_case_name = "Loan Default by client"
use_case_description = (
    "Predict clients' payment difficulties the next 6 months for new loan based on its "
    "application data and prior credit history."
)
fb.UseCase.create(
    name=use_case_name,
    target_name=target_name,
    context_name=context_name,
    description=use_case_description,
)

use_case_name = "Loan Default by client" use_case_description = ( "Predict clients' payment difficulties the next 6 months for new loan based on its " "application data and prior credit history." ) fb.UseCase.create( name=use_case_name, target_name=target_name, context_name=context_name, description=use_case_description, )

Out[4]:

Use Case

name	Loan Default by client
created_at	2025-10-15 08:59:24
updated_at	None
description	Predict clients' payment difficulties the next 6 months for new loan based on its application data and prior credit history.
author	None
primary_entities	name serving_names catalog_name 0 New Application [SK_ID_CURR] Credit Default Dataset SDK Tutorial
context_name	New Loan Application
target_name	Loan_Default
default_eda_table	None
default_preview_table	None

List targets, contexts and use cases in the catalog¶

In [5]:

catalog.list_targets()

catalog.list_targets()

Out[5]:

	id	name	dtype	entities	created_at
0	None	Loan_Default	INT	[New Application]	2025-10-15T08:59:24.338000

In [6]:

catalog.list_contexts()

catalog.list_contexts()

Out[6]:

	id	name	primary_entity_ids	description
0	68ef626b11c7e23eceb087f0	New Loan Application	[68ef62316bb594c3d5cc80b4]	Loan application under review.

In [7]:

catalog.list_use_cases()

catalog.list_use_cases()

Out[7]:

	id	name	default_preview_table_name	default_eda_table_name	description
0	68ef626c11c7e23eceb087f3	Loan Default by client	None	None	Predict clients' payment difficulties the next...

Concepts discussed in this tutorial¶

• More about feature definition file

SDK reference for¶

• Target
• Context
• Use Case
• View.groupby()
• GroupBy.forward_aggregate()
• Aggregation functions
• Target.definition