Adding a Surrogate Model
Fiddler’s explainability features require a model on the backend that can generate explanations for you.
📘 If you don't want to or cannot upload your actual model file, Surrogate Models serve as a way for Fiddler to generate approximate explanations.
A surrogate model is built automatically when you call add_surrogate
on an existing model that has a baseline dataset defined. You just need to provide a few key details on how your model operates during onboarding.
Surrogate Model prerequisites:
An onboarded model with:
A defined model task (regression, binary classification, etc.)
A target column (ground truth labels)
An output column (model predictions)
Model feature columns
A baseline dataset
Update the artifact
DATASET_NAME = 'YOUR_DATASET_NAME'
dataset = fdl.Dataset.from_name(name=DATASET_NAME, model_id=model.id)
job = model.add_surrogate(
dataset_id=dataset.id
)
job.wait()
💡 Need help? Contact us at [email protected].
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