Training Sets

The Training Sets page lets you upload labelled datasets of past Trustfull lookups to measure how accurately the model's scores matched real-world outcomes for your traffic. Each training set is the input for generating Model Insights, a performance analysis of your scoring model.

How to Upload a Training Set

Uploading a training set takes three steps: prepare a CSV file with your labelled lookups, fill in the required fields in the upload modal, and validate the file before confirming the upload.

Prepare your CSV

The CSV must contain lookups from a single App Key and a single product. Each row represents one past Trustfull lookup and requires two columns:

• customer_id: the identifier used in the original lookup
• label:
  • okfor confirmed genuine customers;
  • ko confirmed fraudolent customers

You can download a prefilled CSV template from the upload modal by clicking Download CSV template.

Understanding Genuine Customers vs Fraudolents

--> spiegare differenza tra real genuine e fraudolent customer con esempi

Open the upload modal and fill in the required fields

Click Upload Training Set in the top right corner. In the modal, provide:

• App Key: the app key used to run the original lookups
• Training Set Name: a label to identify this dataset in the list
• Select Product: the product the lookups were run against (e.g. Session, Phone)

Once all fields are filled, select your CSV file from the upload area.

Validate and upload

After selecting your file, validation runs automatically. If the file is valid, a confirmation banner shows the total record count along with the OK and KO breakdown.

Click Upload to complete the process. The training set appears in the list with its name, product, size, OK/KO counts, fraud rate, and creation date.

Next: Generate Insights or Create a Model

Once a training set is uploaded, you can use it in two ways directly from the list. Click the context menu on any training set row to:

• Run Insights: generates a performance analysis of how well the active model's scores matched the labelled outcomes in your dataset. Results appear in the Model Insights page.
• Create Model: generates a custom model with rules automatically derived from the fraud patterns in your data. The model appears in the Model Library ready for review and publishing.